Thus far, we have only focused on measure and integration theory in the context of Euclidean spaces . Now, we will work in a more abstract and general setting, in which the Euclidean space is replaced by a more general space .

It turns out that in order to properly define measure and integration on a general space , it is not enough to just specify the set . One also needs to specify two additional pieces of data:

- A collection of subsets of that one is allowed to measure; and
- The measure one assigns to each measurable set .

For instance, Lebesgue measure theory covers the case when is a Euclidean space , is the collection of all Lebesgue measurable subsets of , and is the Lebesgue measure of .

The collection has to obey a number of axioms (e.g. being closed with respect to countable unions) that make it a -algebra, which is a stronger variant of the more well-known concept of a boolean algebra. Similarly, the measure has to obey a number of axioms (most notably, a countable additivity axiom) in order to obtain a measure and integration theory comparable to the Lebesgue theory on Euclidean spaces. When all these axioms are satisfied, the triple is known as a measure space. These play much the same role in abstract measure theory that metric spaces or topological spaces play in abstract point-set topology, or that vector spaces play in abstract linear algebra.

On any measure space, one can set up the unsigned and absolutely convergent integrals in almost exactly the same way as was done in the previous notes for the Lebesgue integral on Euclidean spaces, although the approximation theorems are largely unavailable at this level of generality due to the lack of such concepts as “elementary set” or “continuous function” for an abstract measure space. On the other hand, one *does* have the fundamental convergence theorems for the subject, namely Fatou’s lemma, the monotone convergence theorem and the dominated convergence theorem, and we present these results here.

One question that will not be addressed much in this current set of notes is how one actually *constructs* interesting examples of measures. We will discuss this issue more in later notes (although one of the most powerful tools for such constructions, namely the Riesz representation theorem, will not be covered until 245B).

** — 1. Boolean algebras — **

We begin by recalling the concept of a Boolean algebra.

Definition 1 (Boolean algebras)Let be a set. A (concrete)Boolean algebraon is a collection of which obeys the following properties:

- (Empty set) .
- (Complement) If , then the complement also lies in .
- (Finite unions) If , then .
We sometimes say that is

-measurable, ormeasurable with respect to, if .Given two Boolean algebras on , we say that is

finer than,a sub-algebra of, ora refinement of, or that iscoarser thanora coarsening of, if .

We have chosen a “minimalist” definition of a Boolean algebra, in which one is only assumed to be closed under two of the basic Boolean operations, namely complement and finite union. However, by using the laws of Boolean algebra (such as de Morgan’s laws), it is easy to see that a Boolean algebra is also closed under other Boolean algebra operations such as intersection , set differerence , and symmetric difference . So one could have placed these additional closure properties inside the definition of a Boolean algebra without any loss of generality. However, when we are *verifying* that a given collection of sets is indeed a Boolean algebra, it is convenient to have as minimal a set of axioms as possible. (This point is discussed further in this Math Overflow comment of mine.)

Remark 1One can also considerabstract Boolean algebras, which do not necessarily live in an ambient domain , but for which one has a collection of abstract Boolean operations such as meet and join instead of the concrete operations of intersection and union . We will not take this abstract perspective here, but see this blog post of mine for some further discussion of the relationship between concrete and abstract Boolean algebras, which is codified by Stone’s theorem.

Example 1 (Trivial and discrete algebra)Given any set , the coarsest Boolean algebra is thetrivial algebra, in which the only measurable sets are the empty set and the whole set. The finest Boolean algebra is thediscrete algebra, in whicheveryset is measurable. All other Boolean algebras are intermediate between these two extremes: finer than the trivial algebra, but coarser than the discrete one.

Exercise 1 (Elementary algebra)Let be the collection of those sets that are either elementary sets, or co-elementary sets (i.e. the complement of an elementary set). Show that is a Boolean algebra. We will call this algebra theelementary Boolean algebraof .

Example 2 (Jordan algebra)Let be the collection of subsets of that are either Jordan measurable or co-Jordan measurable (i.e. the complement of a Jordan measurable set). Then is a Boolean algebra that is finer than the elementary algebra. We refer to this algebra as theJordan algebraon (but caution that there is a completely different concept of a Jordan algebra in mathematics.)

Example 3 (Lebesgue algebra)Let be the collection of Lebesgue measurable subsets of . Then is a Boolean algebra that is finer than the Jordan algebra; we refer to this as theLebesgue algebraon .

Example 4 (Null algebra)Let be the collection of subsets of that are either Lebesgue null sets or Lebesgue co-null sets (the complement of null sets). Then is a Boolean algebra that is coarser than the Lebesgue algebra; we refer to it as thenull algebraon .

Exercise 2 (Restriction)Let be a Boolean algebra on a set , and let be a subset of (not necessarily -measurable). Show that therestrictionof to is a Boolean algebra on . If is -measurable, show that

Example 5 (Atomic algebra)Let be partitioned into a union of disjoint sets , which we refer to asatoms. Then this partition generates a Boolean algebra , defined as the collection of all the sets of the form for some , i.e. is the collection of all sets that can be represented as the union of one or more atoms. This is easily verified to be a Boolean algebra, and we refer to it as theatomic algebrawith atoms . The trivial algebra corresponds to the trivial partition into a single atom; at the other extreme, the discrete algebra corresponds to the discrete partition into singleton atoms. More generally, note that finer (resp. coarser) partitions lead to finer (resp. coarser) atomic algebra. In this definition, we permit some of the atoms in the partition to be empty; but it is clear that empty atoms have no impact on the final atomic algebra, and so without loss of generality one can delete all empty atoms and assume that all atoms are non-empty if one wishes.

Example 6 (Dyadic algebras)Let be an integer. Thedyadic algebraat scale in is defined to be the atomic algebra generated by the half-open dyadic cubesof length (see Exercise 14 of the prologue). These are Boolean algebras which are increasing in : . Draw a diagram to indicate how these algebras sit in relation to the elementary, Jordan, and Lebesgue, null, discrete, and trivial algebras.

Remark 2The dyadic algebras are analogous to the finite resolution one has on modern computer monitors, which subdivide space into square pixels. A low resolution monitor (in which each pixel has a large size) can only resolve a very small set of “blocky” images, as opposed to the larger class of images that can be resolved by a finer resolution monitor.

Exercise 3Show that the non-empty atoms of an atomic algebra are determined up to relabeling. More precisely, show that if are two partitions of into non-empty atoms , , then if and only if exists a bijection such that for all .

While many Boolean algebras are atomic, many are not, as the following two exercises indicate.

Exercise 4Show that every finite Boolean algebra is an atomic algebra. (A Boolean algebra isfiniteif its cardinality is finite, i.e. there are only finitely many measurable sets.) Conclude that every finite Boolean algebra has a cardinality of the form for some natural number . From this exercise and Exercise 3 we see that there is a one-to-one correspondence between finite Boolean algebras on and finite partitions of into non-empty sets (up to relabeling).

Exercise 5Show that the elementary, Jordan, Lebesgue, and null algebras arenotatomic algebras. (Hint:argue by contradiction. If these algebras were atomic, what must the atoms be?)

Now we describe some further ways to generate Boolean algebras.

Exercise 6 (Intersection of algebras)Let be a family of Boolean algebras on a set , indexed by a (possibly infinite or uncountable) label set . Show that the intersection of these algebras is still a Boolean algebra, and is the finest Boolean algebra that is coarser than all of the . (If is empty, we adopt the convention that is the discrete algebra.)

Definition 2 (Generation of algebras)Let be any family of sets in . We define to be the intersection of all the Boolean algebras that contain , which is again a Boolean algebra by Exercise 6. Equivalently, is the coarsest Boolean algebra that contains . We say that is the Boolean algebrageneratedby .

Example 7is a Boolean algebra if and only if ; thus each Boolean algebra is generated by itself.

Exercise 7Show that the elementary algebra is generated by the collection of boxes in .

Exercise 8Let be a natural number. Show that if is a finite collection of sets, then is a finite Boolean algebra of cardinality at most (in particular, finite sets generate finite algebras). Give an example to show that this bound is best possible. (Hint: for the latter, it may be convenient to use a discrete ambient space such as the discrete cube .)

The Boolean algebra can be described explicitly in terms of as follows:

Exercise 9 (Recursive description of a generated Boolean algebra)Let be a collection of sets in a set . Define the sets recursively as follows:

- .
- For each , we define to be the collection of all sets that either the union of a finite number of sets in (including the empty union ), or the complement of such a union.
Show that .

** — 2. -algebras and measurable spaces — **

In order to obtain a measure and integration theory that can cope well with limits, the finite union axiom of a Boolean algebra is insufficient, and must be improved to a countable union axiom:

Definition 3 (Sigma algebras)Let be a set. A -algebra on is a collection of which obeys the following properties:

- (Empty set) .
- (Complement) If , then the complement also lies in .
- (Countable unions) If , then .
We refer to the pair of a set together with a -algebra on that set as a

measurable space.

Remark 3The prefix usually denotes “countable union”. See also the concepts of a -compact topological space, or a -finite measure space, or set for other instances of this prefix.

From de Morgan’s law (which is just as valid for infinite unions and intersections as it is for finite ones), we see that -algebras are closed under countable intersections as well as countable unions.

By padding a finite union into a countable union by using the empty set, we see that every -algebra is automatically a Boolean algebra. Thus, we automatically inherit the notion of being measurable with respect to a -algebra, or of one -algebra being coarser or finer than another.

Exercise 10Show that all atomic algebras are -algebras. In particular, the discrete algebra and trivial algebra are -algebras, as are the finite algebras and the dyadic algebras on Euclidean spaces.

Exercise 11Show that the Lebesgue and null algebras are -algebras, but the elementary and Jordan algebras are not.

Exercise 12Show that any restriction of a -algebra to a subspace of (as defined in Exercise 2) is again a -algebra on the subspace .

There is an exact analogue of Exercise 6:

Exercise 13 (Intersection of -algebras)Show that the intersection of an arbitrary (and possibly infinite or uncountable) number of -algebras is again a -algebra, and is the finest -algebra that is coarser than all of the .

Similarly, we have a notion of generation:

Definition 4 (Generation of -algebras)Let be any family of sets in . We define to be the intersection of all the -algebras that contain , which is again a -algebra by Exercise 13. Equivalently, is the coarsest -algebra that contains . We say that is the -algebrageneratedby .

Since every -algebra is a Boolean algebra, we have the trivial inclusion

However, equality need not hold; it only holds if and only if is a -algebra. For instance, if is the collection of all boxes in , then is the elementary algebra (Exercise 7), but cannot equal this algebra, as it is not a -algebra.

Remark 4From the definitions, it is clear that we have the following principle, somewhat analogous to the principle of mathematical induction: if is a family of sets in , and is a property of sets which obeys the following axioms:

- is true.
- is true for all .
- If is true for some , then is true also.
- If are such that is true for all , then is true also.
Then one can conclude that is true for all . Indeed, the set of all for which holds is a -algebra that contains , whence the claim. This principle is particularly useful for establishing properties of Borel measurable sets (see below).

We now turn to an important example of a -algebra:

Definition 5 (Borel -algebra)Let be a metric space, or more generally a topological space. The Borel -algebra of is defined to be the -algebra generated by the open subsets of . Elements of will be calledBorel measurable.

Thus, for instance, the Borel -algebra contains the open sets, the closed sets (which are complements of open sets), the countable unions of closed sets (i.e. sets), the countable intersections of open sets (i.e. sets), the countable intersections of sets, and so forth.

In , every open set is Lebesgue measurable, and so we see that the Borel -algebra is coarser than the Lebesgue -algebra. We will shortly see, though, that the two -algebras are not equal.

We defined the Borel -algebra to be generated by the open sets. However, they are also generated by several other sets:

Exercise 14Show that the Borel -algebra of a Euclidean set is generated by any of the following collections of sets:

- The open subsets of .
- The closed subsets of .
- The compact subsets of .
- The open balls of .
- The boxes in .
- The elementary sets in .
(

Hint:To show that two families of sets generate the same -algebra, it suffices to show that every -algebra that contains , contains also, and conversely.)

There is an analogue of Exercise 9, which illustrates the extent to which a generated -algebra is “larger” than the analogous generated Boolean algebra:

Exercise 15 (Recursive description of a generated -algebra)(This exercise requires familiarity with the theory of ordinals, which is reviewed here. Recall that we are assuming the axiom of choice throughout this course.) Let be a collection of sets in a set , and let be the first uncountable ordinal. Define the sets for every countable ordinal via transfinite induction as follows:

- .
- For each countable successor ordinal , we define to be the collection of all sets that either the union of an at most countable number of sets in (including the empty union ), or the complement of such a union.
- For each countable limit ordinal , we define .
Show that .

Remark 5The first uncountable ordinal will make several further cameo appearances throughout this course, for instance by generating counterexamples to various plausible statements in point-set topology. In the case when is the collection of open sets in a topological space, so that , then the sets are essentially the Borel hierarchy (which starts at the open and closed sets, then moves on to the and sets, and so forth); these play an important role in descriptive set theory.

Exercise 16(This exercise requires familiarity with the theory of cardinals.) Let be an infinite family of subsets of of cardinality (thus is an infinite cardinal). Show that has cardinality at most . (Hint:use Exercise 15.) In particular, show that the Borel -algebra has cardinality at most .Conclude that there exist Jordan measurable (and hence Lebesgue measurable) subsets of which are not Borel measurable. (

Hint:How many subsets of the Cantor set are there?) Use this to place the Borel -algebra on the diagram that you drew for Exercise 6.

Remark 6Despite this demonstration that not all Lebesgue measurable subsets are Borel measurable, it is remarkably difficult (though not impossible) to exhibit aspecificset that is not Borel measurable. Indeed, a large majority of the explicitly constructible sets that one actually encounters in practice tend to be Borel measurable, and one can view the property of Borel measurability intuitively as a kind of “constructibility” property. (Indeed, as averycrude first approximation, one can view the Borel measurable sets as those sets of “countable descriptive complexity”; in contrast, sets of finite descriptive complexity tend to be Jordan measurable (assuming they are bounded, of course).

Exercise 17Let be Borel measurable subsets of respectively. Show that is a Borel measurable subset of . (Hint:first establish this in the case when is a box, by using Remark 4. To obtain the general case, apply Remark 4 yet again.)

The above exercise has a partial converse:

Exercise 18Let be a Borel measurable subset of .

- Show that for any , the slice is a Borel measurable subset of . Similarly, show that for every , the slice is a Borel measurable subset of .
- Give a counterexample to show that this claim is
nottrue if “Borel” is replaced with “Lebesgue” throughout. (Hint:the Cartesian product of any set with a point is a null set, even if the first set was not measurable.)

Exercise 19Show that the Lebesgue -algebra on is generated by the union of the Borel -algebra and the null -algebra.

** — 3. Countably additive measures and measure spaces — **

Having set out the concept of a -algebra a measurable space, we now endow these structures with a measure.

We begin with the finitely additive theory, although this theory is too weak for our purposes and will soon be supplanted by the countably additive theory.

Definition 6 (Finitely additive measure)Let be a Boolean algebra on a space . An (unsigned)finitely additive measureon is a map that obeys the following axioms:

- (Empty set) .
- (Finite additivity) Whenever are disjoint, then .

Remark 7The empty set axiom is needed in order to rule out the degenerate situation in which every set (including the empty set) has infinite measure.

Example 8Lebesgue measure is a finitely additive measure on the Lebesgue -algebra, and hence on all sub-algebras (such as the null algebra, the Jordan algebra, or the elementary algebra). In particular, Jordan measure and elementary measure are finitely additive (adopting the convention that co-Jordan measurable sets have infinite Jordan measure, and co-elementary sets have infinite elementary measure).On the other hand, as we saw in previous notes, Lebesgue outer measure is

notfinitely additive on the discrete algebra, and Jordan outer measure isnotfinitely additive on the Lebesgue algebra.

Example 9 (Dirac measure)Let and be an arbitrary Boolean algebra on . Then the Dirac measure at , defined by setting , is finitely additive.

Example 10 (Zero measure)The zero measure is a finitely additive measure on any Boolean algebra.

Example 11 (Linear combinations of measures)If is a Boolean algebra on , and are finitely additive measures on , then is also a finitely additive measure, as is for any . Thus, for instance, the sum of Lebesgue measure and a Dirac measure is also a finitely additive measure on the Lebesgue algebra (or on any of its sub-algebras).

Example 12 (Restriction of a measure)If is a Boolean algebra on , is a finitely additive measure, and is a-measurablesubset of , then the restriction of to , defined by setting whenever (i.e. if and ), is also a finitely additive meaure.

Example 13 (Counting measure)If is a Boolean algebra on , then the function defined by setting to be the cardinality of if is finite, and if is infinite, is a finitely additive measure, known ascounting measure.

As with our definition of Boolean algebras and -algebras, we adopted a “minimalist” definition so that the axioms are easy to verify. But they imply several further useful properties:

Exercise 20Let be a finitely additive measure on a Boolean -algebra . Establish the following facts:

- (Monotonicity) If are -measurable and , then .
- (Finite additivity) If is a natural number, and are -measurable and disjoint, then .
- (Finite sub-additivity) If is a natural number, and are -measurable, then .
- (Inclusion-exclusion for two sets) If are -measurable, then .
(

Caution:remember that the cancellation law does not hold in if is infinite, and so the use of cancellation (or subtraction) should be avoided if possible.)

One can characterise measures completely for any finite algebra:

Exercise 21Let be a finite Boolean algebra, generated by a finite family of non-empty atoms. Show that for every finitely additive measure on there exists such thatEquivalently, if is a point in for each , then

Furthermore, show that the are uniquely determined by .

This is about the limit of what one can say about finitely additive measures at this level of generality. We now specialise to the *countably additive measures* on -algebras.

Definition 7 (Countably additive measure)Let be a measurable space. An (unsigned)countably additive measureon , ormeasurefor short, is a map that obeys the following axioms:

- (Empty set) .
- (Countable additivity) Whenever are a countable sequence of disjoint measurable sets, then .
A triplet , where is a measurable space and is a countably additive measure, is known as a measure space.

Note the distinction between a measure space and a measurable space. The latter has the *capability* to be equipped with a measure, but the former is *actually* equipped with a measure.

Example 14Lebesgue measure is a countably additive measure on the Lebesgue -algebra, and hence on every sub--algebra (such as the Borel -algebra).

Example 15The Dirac measures from Exercise 9 are countably additive, as is counting measure.

Example 16Any restriction of a countably additive measure to a measurable subspace is again countably additive.

Exercise 22 (Countable combinations of measures)Let be a measurable space.

- If is a countably additive measure on , and , then is also countably additive.
- If are a sequence of countably additive measures on , then the sum is also a countably additive measure.

Note that countable additivity measures are necessarily finitely additive (by padding out a finite union into a countable union using the empty set), and so countably additive measures inherit all the properties of finitely additive properties, such as monotonicity and finite subadditivity. But one also has additional properties:

Exercise 23Let be a measure space.

- (Countable subadditivity) If are -measurable, then .
- (Upwards monotone convergence) If are -measurable, then
- (Downwards monotone convergence) If are -measurable, and for at least one , then
Show that the downward monotone convergence claim can fail if the hypothesis that for at least one is dropped. (

Hint:copy the argument used for Exercise 10 in Notes 1.)

Exercise 24 (Dominated convergence for sets)Let be a measure space. Let be a sequence of -measurable sets that converge to another set , in the sense that converges pointwise to .

- Show that is also -measurable.
- If there exists a -measurable set of finite measure (i.e. ) that contains all of the , show that . (
Hint:Apply downward monotonicity to the sets .)- Show that the previous part of this exercise can fail if the hypothesis that all the are contained in a set of finite measure is omitted.

Exercise 25Let be an at most countable set with the discrete -algebra. Show that every measure on this measurable space can be uniquely represented in the formfor some , thus

for all . (This claim fails in the uncountable case, although showing this is slightly tricky.)

A *null set* of a measure space is defined to be a -measurable set of measure zero. A *sub-null set* is any subset of a null set. A measure space is said to be complete if every sub-null set is a null set. Thus, for instance, the Lebesgue measure space is complete, but the Borel measure space is not (as can be seen from the solution to Exercise 16).

Completion is a convenient property to have in some cases, particularly when dealing with properties that hold almost everywhere. Fortunately, it is fairly easy to modify any measure space to be complete:

Exercise 26 (Completion)Let be a measure space. Show that there exists a unique refinement , known as the completion of , which is the coarsest refinement of that is complete. Furthermore, show that consists precisely of those sets that differ from a -measurable set by a -subnull set.

Exercise 27Show that the Lebesgue measure space is the completion of the Borel measure space .

** — 4. Measurable functions, and integration on a measure space — **

Now we are ready to define integration on measure spaces. We first need the notion of a *measurable function*, which is analogous to that of a continuous function in topology. Recall that a function between two topological spaces is continuous if the inverse image of any open set is open. In a similar spirit, we have

Definition 8Let be a measurable space, and let or be an unsigned or complex-valued function. We say that ismeasurableif is -measurable for every open subset of or .

From Lemma 7 of Notes 2, we see that this generalises the notion of a Lebesgue measurable function.

Exercise 28Let be a measurable space.

- Show that a function is measurable if and only if the level sets are -measurable.
- Show that an indicator function of a set is measurable if and only if itself is -measurable.
- Show that a function or is measurable if and only if is -measurable for every Borel-measurable subset of or .
- Show that a function is measurable if and only if its real and imaginary parts are measurable.
- Show that a function is measurable if and only if the magnitudes , of its positive and negative parts are measurable.
- If are a sequence of measurable functions that converge pointwise to a limit , then show that is also measurable. Obtain the same claim if is replaced by .
- If is measurable and is continuous, show that is measurable. Obtain the same claim if is replaced by .
- Show that the sum or product of two measurable functions in or is still measurable.

Remark 8One can also view measurable functions in a more category theoretic fashion. Definemeasurable morphismormeasurable mapfrom one measurable space to another to be a function with the property that is -measurable for every -measurable set . Then a measurable function or is the same thing as a measurable morphism from to or , where the latter is equipped with the Borel -algebra. Also, one -algebra on a space is coarser than another precisely when the identity map is a measurable morphism from to . The main purpose of adopting this viewpoint is that it is obvious that the composition of measurable morphisms is again a measurable morphism. This is important in those fields of mathematics, such as ergodic theory, in which one frequently wishes to compose measurable transformations (and in particular, to compose a transformation with itself repeatedly); but it will not play a major role in this course.

Measurable functions are particularly easy to describe on atomic spaces:

Exercise 29Let be a measurable space that is atomic, thus for some partition of into disjoint non-empty atoms. Show that a function or is measurable if and only if it is constant on each atom, or equivalently if one has a representation of the formfor some constants in or in as appropriate. Furthermore, the are uniquely determined by .

Exercise 30 (Egorov’s theorem)Let be a finite measure space (so ), and let be a sequence of measurable functions that converge pointwise almost everywhere to a limit , and let . Show that there exists a measurable set of measure at most such that converges uniformly to outside of . Give an example to show that the claim can fail when the measure is not finite.

In Notes 2 we defined first an simple integral, then an unsigned integral, and then finally an absolutely convergent integral. We perform the same three stages here. We begin with the simple integral in the case when the -algebra is finite:

Definition 9 (Simple integral)Let be a measure space with finite. By Exercise 4, is partitioned into a finite number of atoms . If is measurable, then by Exercise 29 it has a unique representation of the formfor some . We then define the

simple integralof by the formulaNote that, thanks to Exercise 3, the precise decomposition into atoms does not affect the definition of the simple integral.

One could also define a simple integral for absolutely convergent complex-valued functions on a measurable space with a finite -algebra, but we will not need to do so here.

With this definition, it is clear that one has the monotonicity property

whenever are unsigned measurable, as well as the linearity properties

and

for unsigned measurable and . We also make the following important technical observation:

Exercise 31 (Simple integral unaffected by refinements)Let be a measure space, and let be arefinementof , which means that contains and agrees with on . Suppose that both are finite, and let be measurable. Show that

This allows one to extend the simple integral to simple functions:

Definition 10 (Integral of simple functions)An (unsigned)simple functionon a measurable space is a measurable function that takes on finitely many values . Note that such a function is then automatically measurable with respect to at least one finite sub--algebra of , namely the -algebra generated by the preimages of . We then define the simple integral by the formulawhere is the restriction of to .

Note that there could be multiple finite -algebras with respect to which is measurable, but Exercise 31 guarantees that all such extensions will give the same simple integral. Indeed, if were measurable with respect to two separate finite sub--algebras and of , then it would also be measurable with respect to their common refinement , which is also finite (by Exercise 8), and then by Exercise 31, and are both equal to , and hence equal to each other.

From this we can deduce the following properties of the simple integral. As with the Lebesgue theory, we say that a property of an element of a measure space holds *-almost everywhere* if it holds outside of a sub-null set.

Exercise 32 (Basic properties of the simple integral)Let be a measure space, and let be simple functions.

- (Monotonicity) If pointwise, then .
- (Compatibility with measure) For every -measurable set , we have .
- (Homogeneity) For every , one has .
- (Finite additivity) .
- (Insensitivity to refinement) If is a refinement of (as defined in Exercise 31), then .
- (Almost everywhere equivalence) If for -almost every , then .
- (Finiteness) if and only if is finite almost everywhere, and is supported on a set of finite measure.
- (Vanishing) if and only if is zero almost everywhere.

Exercise 33 (Inclusion-exclusion principle)Let be a measure space, and let be -measurable sets of finite measure. Show that(

Hint:Compute in two different ways.)

Remark 9The simple integral could also be defined on finitely additive measure spaces, rather than countably additive ones, and all the above properties would still apply. However, on a finitely additive measure space one would have difficulty extending the integral beyond simple functions, as we will now do.

From the simple integral, we can now define the unsigned integral, similarly to what was done for the unsigned Lebesgue integral in Notes 2:

Definition 11Let be a measure space, and let be measurable. Then we define the unsigned integral of by the formula

Clearly, this definition generalises the corresponding definition in Definition 10 of Notes 2. Indeed, if is Lebesgue measurable, then .

We record some easy properties of this integral:

Exercise 34 (Easy properties of the unsigned integral)Let be a measure space, and let be measurable.

- (Almost everywhere equivalence) If -almost everywhere, then
- (Monotonicity) If -almost everywhere, then .
- (Homogeneity) We have for every .
- (Superadditivity) We have .
- (Compatibility with the simple integral) If is simple, then .
- (Markov’s inequality) For any , one has
In particular, if , then the sets have finite measure for each .

- (Finiteness) If , then is finite for -almost every .
- (Vanishing) If , then is zero for -almost every .
- (Horizontal truncation) We have .
- (Vertical truncation) If is an increasing sequence of -measurable sets, then
- (Restriction) If is a measurable subset of , then , where is the restriction of to , and the restriction was defined in Example 12. We will often abbreviate (by slight abuse of notation) as .

As before, one of the key properties of this integral is its additivity:

Theorem 12Let be a measure space, and let be measurable. Then

*Proof:* In view of super-additivity, it suffices to establish the sub-additivity property

We establish this in stages. We first deal with the case when is a *finite* measure (which means that ) and are bounded. Pick an , and let be rounded down to the nearest integer multiple of , and be rounded up to the nearest integer multiple. Clearly, we have the pointwise bounds

and

Since is bounded, and are simple. Similarly define . We then have the pointwise bound

hence by Exercise 34 and the properties of the simple integral,

From (1) we conclude that

Letting and using the assumption that is finite, we obtain the claim.

Now we continue to assume that is a finite measure, but now do not assume that are bounded. Then for any natural number , we can use the previous case to deduce that

Since , we conclude that

Taking limits as using horizontal truncation, we obtain the claim.

Finally, we no longer assume that is of finite measure, and also do not require to be bounded. If either or is infinite, then by monotonicity, is infinite as well, and the claim follows; so we may assume that and are both finite. By Markov’s inequality, we conclude that for each natural number , the set has finite measure. These sets are increasing in , and are supported on , and so by vertical truncation

From the previous case, we have

Letting and using vertical truncation we obtain the claim.

Exercise 35 (Linearity in )Let be a measure space, and let be measurable.

- Show that for every .
- If are a sequence of measures on , show that

Exercise 36 (Change of variables formula)Let be a measure space, and let be a measurable morphism (as defined in Remark 8) from to another measurable space . Define the pushforward of by by the formula .

- Show that is a measure on , so that is a measure space.
- If is measurable, show that .
(

Hint:the quickest proof here is via the monotone convergence theorem below, but it is also possible to prove the exercise without this theorem.)

Exercise 37Let be an invertible linear transformation, and let be Lebesgue measure on . Show that , where the pushforward of was defined in Exercise 36.

Exercise 38 (Sums as integrals)Let be an arbitrary set (with the discrete -algebra), let be counting measure (see Exercise 13), and let be an arbitrary unsigned function. Show that is measurable with

Once one has the unsigned integral, one can define the absolutely convergent integral exactly as in the Lebesgue case:

Definition 13 (Absolutely convergent integral)Let be a measure space. A measurable function is said to beabsolutely integrableif the unsigned integralis finite, and use , , or to denote the space of absolutely integrable functions. If is real-valued and absolutely integrable, we define the integral by the formula

where , are the magnitudes of the positive and negative components of . If is complex-valued and absolutely integrable, we define the integral by the formula

where the two integrals on the right are interpreted as real-valued integrals. It is easy to see that the unsigned, real-valued, and complex-valued integrals defined in this manner are compatible on their common domains of definition.

Clearly, this definition generalises the corresponding definition in Definition 13 of Notes 2.

We record some of the key facts about the absolutely convergent integral:

Exercise 39Let be a measure space.

- Show that is a complex vector space.
- Show that the integration map is a complex-linear map from to .
- Establish the triangle inequality and the homogeneity property for all and .
- Show that if are such that for -almost every , then .
- If , and is a refinement of , then , and . (
Hint:it is easy to get one inequality. To get the other inequality, first work in the case when is both bounded and has finite measure support (i.e. is both vertically and horizontally truncated).)- Show that if , then if and only if is zero -almost everywhere.
- If is -measurable and , then and . As before, by abuse of notation we write for .

** — 5. The convergence theorems — **

Let be a measure space, and let be a sequence of measurable functions. Suppose that as , converges pointwise either everywhere, or -almost everywhere, to a measurable limit . A basic question in the subject is to determine the conditions under which such pointwise convergence would imply convergence of the integral:

To put it another way: when can we ensure that one can interchange integrals and limits,

There are certainly some cases in which one can safely do this:

Exercise 40 (Uniform convergence on a finite measure space)Suppose that is afinitemeasure space (so ), and (resp. ) are a sequence of unsigned measurable functions (resp. absolutely integrable functions) that converge uniformly to a limit . Show that converges to .

However, there are also cases in which one cannot interchange limits and integrals, even when the are unsigned. We give the three classic examples, all of “moving bump” type, though the way in which the bump moves varies from example to example:

Example 17 (Escape to horizontal infinity)Let be the real line with Lebesgue measure, and let . Then converges pointwise to , but does not converge to . Somehow, all the mass in the has escaped by moving off to infinity in a horizontal direction, leaving none behind for the pointwise limit .

Example 18 (Escape to width infinity)Let be the real line with Lebesgue measure, and let . Then now convergesuniformly, but still does not converge to . Exercise 40 would prevent this from happening if all the were supported in a single set of finite measure, but the increasingly wide nature of the support of the prevents this from happening.

Example 19 (Escape to vertical infinity)Let be the unit interval with Lebesgue measure (restricted from ), and let . Now, we have finite measure, and converges pointwise to , but no uniform convergence. And again, is not converging to . This time, the mass has escaped vertically, through the increasingly large values of .

Remark 10From the perspective of time-frequency analysis (or perhaps more accurately, space-frequency analysis), these three escapes are analogous (though not quite identical) to escape to spatial infinity, escape to zero frequency, and escape to infinite frequency respectively, thus describing the three different ways in which phase space fails to be compact (if one excises the zero frequency as being singular).

However, once one shuts down these avenues of escape to infinity, it turns out that one can recover convergence of the integral. There are two major ways to accomplish this. One is to enforce *monotonicity*, which prevents each from abandoning the location where the mass of the preceding was concentrated and which thus shuts down the above three escape scenarios. More precisely, we have the monotone convergence theorem:

Theorem 14 (Monotone convergence theorem)Let be a measure space, and let be a monotone non-decreasing sequence of unsigned measurable functions on . Then we have

Note that in the special case when each is an indicator function , this theorem collapses to the upwards monotone convergence property (Exercise 23.2). Conversely, the upwards monotone convergence property will play a key role in the proof of this theorem.

*Proof:* Write , then is measurable. Since the are non-decreasing to , we see from monotonicity that are non-decreasing and bounded above by , which gives the bound

It remains to establish the reverse inequality

By definition, it suffices to show that

whenever is a simple function that is bounded pointwise by . By horizontal truncation we may assume without loss of generality that also is finite everywhere, then we can write

for some and some disjoint -measurable sets , thus

Let be arbitrary. Then we have

for all . Thus, if we define the sets

then the increase to and are measurable. By upwards monotonicity of measure, we conclude that

On the other hand, observe the pointwise bound

for any ; integrating this, we obtain

Taking limits as , we obtain

sending we then obtain the claim.

Remark 11It is easy to see that the result still holds if the monotonicity only holds almost everywhere rather than everywhere.

This has a number of important corollaries. Firstly, we can generalise (part of) Tonelli’s theorem for exchanging sums (see Theorem 2 of Notes 1):

Corollary 15 (Tonelli’s theorem for sums and integrals)Let be a measure space, and let be a sequence of unsigned measurable functions. Then one has

*Proof:* Apply the monotone convergence theorem to the partial sums .

Exercise 41Give an example to show that this corollary can fail if the are assumed to be absolutely integrable rather than unsigned measurable, even if the sum is absolutely convergent for each . (Hint:think about the three escapes to infinity.)

Exercise 42 (Borel-Cantelli lemma)Let be a measure space, and let be a sequence of -measurable sets such that . Show that almost every is contained in at most finitely many of the (i.e. is finite for almost every ). (Hint:Apply Tonelli’s theorem to the indicator functions .)

Exercise 43

- Give an alternate proof of the Borel-Cantelli lemma (Exercise 42) that does not go through any of the convergence theorems, but instead exploits the more basic properties of measure from Exercise 23.
- Give a counterexample that shows that the Borel-Cantelli lemma can fail if the condition is relaxed to .

Secondly, when one does not have monotonicity, one can at least obtain an important inequality, known as Fatou’s lemma:

Corollary 16 (Fatou’s lemma)Let be a measure space, and let be a sequence of unsigned measurable functions. Then

*Proof:* Write for each . Then the are measurable and non-decreasing, and hence by the monotone convergence theorem

By definition of lim inf, we have . By monotonicity, we have for all , and thus

Hence we have

The claim then follows by another appeal to the definition of lim inf.

Remark 12Informally, Fatou’s lemma tells us that when taking the pointwise limit of unsigned functions , that mass can be destroyed in the limit (as was the case in the three key moving bump examples), but it cannot be created in the limit. Of course the unsigned hypothesis is necessary here (consider for instance multiplying any of the moving bump examples by ). While this lemma was stated only for pointwise limits, the same general principle (that mass can be destroyed, but not created, by the process of taking limits) tends to hold for other “weak” notions of convergence. We will see some instances of this in 245B.

Finally, we give the other major way to shut down loss of mass via escape to infinity, which is to *dominate* all of the functions involved by an absolutely convergent one. This result is known as the dominated convergence theorem:

Theorem 17 (Dominated convergence theorem)Let be a measure space, and let be a sequence of measurable functions that converge pointwise -almost everywhere to a measurable limit . Suppose that there is an unsigned absolutely integrable function such that are pointwise -almost everywhere bounded by for each . Then we have

From the moving bump examples we see that this statement fails if there is no absolutely integrable dominating function . The reader is encouraged to see why, in each of the moving bump examples, no such dominating function exists, without appealing to the above theorem. Note also that when each of the is an indicator function , the dominated convergence theorem collapses to Exercise 24.

*Proof:* By modifying on a null set, we may assume without loss of generality that the converge to pointwise everywhere rather than -almost everywhere, and similarly we can assume that are bounded by pointwise everywhere rather than -almost everywhere.

By taking real and imaginary parts we may assume without loss of generality that are real, thus pointwise. Of course, this implies that pointwise also.

If we apply Fatou’s lemma to the unsigned functions , we see that

which on subtracting the *finite* quantity gives

Similarly, if we apply that lemma to the unsigned functions , we obtain

negating this inequality and then cancelling again we conclude that

The claim then follows by combining these inequalities.

Remark 13We deduced the dominated convergence theorem from Fatou’s lemma, and Fatou’s lemma from the monotone convergence theorem. However, one can obtain these theorems in a different order, depending on one’s taste, as they are so closely related. For instance, in Stein-Shakarchi, the logic is somewhat different; one first obtains the slightly simplerbounded convergence theorem, which is the dominated convergence theorem under the assumption that the functions are uniformly bounded and all supported on a single set of finite measure, and then uses that to deduce Fatou’s lemma, which in turn is used to deduce the monotone convergence theorem; and then the horizontal and vertical truncation properties are used to extend the bounded convergence theorem to the dominated convergence theorem. It is instructive to view a couple different derivations of these key theorems to get more of an intuitive understanding as to how they work.

Exercise 44Under the hypotheses of the dominated convergence theorem, establish also that as .

Exercise 45 (Almost dominated convergence)Let be a measure space, and let be a sequence of measurable functions that converge pointwise -almost everywhere to a measurable limit . Suppose that there is an unsigned absolutely integrable functions such that the are pointwise -almost everywhere bounded by , and that as . Show that

Exercise 46 (Defect version of Fatou’s lemma)Let be a measure space, and let be a sequence of unsigned absolutely integrable functions that converges pointwise to an absolutely integrable limit . Show thatas . (

Hint:Apply the dominated convergence theorem to .) Informally, this tells us that the gap between the left and right hand sides of Fatou’s lemma can be measured by the quantity .

Exercise 47Let be a measure space, and let be measurable. Show that the function defined by the formulais a measure. (We will study such measures in greater detail in 245B.)

The monotone convergence theorem is, in some sense, a defining property of the unsigned integral, as the following exercise illustrates.

Exercise 48 (Characterisation of the unsigned integral)Let be a measurable space. be a map from the space of unsigned measurable functions to that obeys the following axioms:

- (Homogeneity) For every and , one has .
- (Finite additivity) For every , one has .
- (Monotone convergence) If are a non-decreasing sequence of unsigned measurable functions, then .
Then there exists a unique measure on such that for all . Furthermore, is given by the formula for all -measurable sets .

Exercise 49Let be a finite measure space (i.e. ), and let be a bounded function. Suppose that is complete, which means that every sub-null set is a null set. Suppose that the upper integraland lower integral

agree. Show that is measurable. (This is a converse to Exercise 11 of Notes 2.)

We will continue to see the monotone convergence theorem, Fatou’s lemma, and the dominated convergence theorem make an appearance throughout the rest of this course sequence.

** — 6. Probability spaces (optional) — **

We now pause to isolate a special type of measure space, namely an probability space. As the name suggests, these spaces are of fundamental importance in the foundations of probability, although it should be emphasised that probability theory should *not* be viewed as the study of probability spaces, as these are merely models for the true objects of study of that theory, namely the behaviour of random events and random variables. (See this post for further discussion of this point.) This course will not be focused on applications to probability theory, but other courses (such as the Math 275 sequence at UCLA) will certainly be taking several results from measure theory (e.g. the Borel-Cantelli lemma, Exercise 42) and transferring them to a probabilistic context in order to apply them to problems of interest in probability theory.

Definition 18 (Probability space)Aprobability spaceis a measure space of total measure : . The measure is known as aprobability measure.

Note the change of notation: whereas measure spaces are traditionally denoted by symbols such as , probability spaces are traditionally denoted by symbols such as . Of course, such notational changes have no impact on the underlying mathematical formalism, but they reflect the different cultures of measure theory and probability theory. In particular, the various components , , carry the following interpretations in probability theory, that are absent in other applications of measure theory:

- The space is known as the sample space, and is interpreted as the set of all possible
*states*that a random system could be in. - The -algebra is known as the
*event space*, and is interpreted as the set of all possible events that one can measure. - The measure of an event is known as the
*probability*of that event.

The various axioms of a probability space then formalise the foundational axioms of probability, as set out by Kolmogorov.

Example 20 (Normalised measure)Given any measure space with , the space is a probability space. For instance, if is a non-empty finite set with the discrete -algebra and the counting measure , then thenormalised counting measureis a probability measure (known as the (discrete) uniform probability measure on ), and is a probability space. In probability theory, this probability spaces models the act of drawing an element of the discrete set uniformly at random.Similarly, if is a Lebesgue measurable set of positive finite Lebesgue measure, , then is a probability space. The probability measure is known as the (continuous) uniform probability measure on . In probability theory, this probability spaces models the act of drawing an element of the continuous set uniformly at random.

Example 21 (Discrete and continuous probability measures)If is a (possibly infinite) non-empty set with the discrete -algebra , and if are a collection of real numbers in with , then the probability measure defined by , or in other wordsis indeed a probability measure, and is a probability space. The function is known as the (discrete) probability distribution of the state variable .

Similarly, if is a Lebesgue measurable subset of of positive (and possibly infinite) measure, and is a Lebesgue measurable function on (where of course we restrict the Lebesgue measure space on to in the usual fashion) with , then is a probability space, where is the measure

The function is known as the (continuous) probability density of the state variable . (This density is not quite unique, since one can modify it on a set of probability zero, but it is well-defined up to this ambiguity. We will return to this point in 245B.)

Exercise 50 (No translation-invariant random integer)Show that there is no probability measure on the integers with the discrete -algebra with the translation-invariance property for every event and every integer .

Exercise 51 (No translation-invariant random real)Show that there is no probability measure on the reals with the Lebesgue -algebra with the translation-invariance property for every event and every real .

Many concepts in measure theory are of importance in probability theory, although the terminology is changed to reflect the different perspective on the subject. For instance, the notion of a property holding almost everywhere is now replaced with that of a property holding almost surely. A measurable function is now referred to as a random variable and is often denoted by symbols such as , and the integral of that function on the probability space (if the random variable is unsigned or absolutely convergent) is known as the expectation of that random variable, and is denoted . Thus, for instance, the Borel-Cantelli lemma (Exercise 42) now reads as follows: given any sequence of events such that , it is almost surely true that at most finitely many of these events hold.

In later notes, when we develop the machinery of product measures and other tools to construct measures, we will see some more interesting examples of probability spaces, which would correspond in probability theory to random processes that are generated by an infinite number of independent random sources.

The following exercise will be moved to a more suitable location in the published version of the notes, but is here currently so as not to disrupt the exercise numbering.

Exercise 52 (Approximation by an algebra)Let be a Boolean algebra on , and let be a measure on .

- If , show that for every and there exists such that .
- More generally, if for some with for all , has finite measure, and , show that there exists such that .

## 99 comments

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26 September, 2010 at 5:24 am

Real Analysis (=Measure Theory) by Terence Tao « UGroh's Weblog[…] und Jordansche Maß Lecture 1: Das Lebesguesche Maß. Lecture 2: Das Lebesguesche Integral Lecture 3: Maße und Maßräume […]

26 September, 2010 at 11:53 am

Anonymousin example 19, integral of is not

[Corrected, thanks – T.]Thanks for the great post

26 September, 2010 at 12:02 pm

Anonymousin exercise 47, domain of is not

[Corrected, thanks – T.]Thanks

26 September, 2010 at 3:24 pm

Anonymousin example 21, is probability distribution or probability density?

thanks,

[Corrected, thanks – T.]26 September, 2010 at 7:51 pm

Laurens GunnarsenDear Professor Tao:

Although the material in this post has long been familiar to me, and although I have generally been able to apply it successfully in contexts that require a working understanding of its details, I confess that I have never been fully satisfied with it.

The sources of my dissatisfaction are various, but perhaps I might begin by asking why it is appropriate to formulate the theory so that it includes “measurable” sets whose measure is “infinity.” As a rule, we tend to regard a function as simply undefined wherever one would naively say that it becomes “infinite;” that is, we typically excise such “bad” points from the function’s domain. Yet this generally sound and widespread practice is abandoned in measure theory, for reasons that (despite much reflection) I have never been able fully to appreciate.

Of course I know perfectly well that allowing the domain of a measure to include sets at which it takes on infinite values is what is always done, and I know that the resulting development “works.” But I have never made my peace with this practice; to me it has always seemed, and still seems, profoundly unnatural.

Can you explain why I am wrong to be dissatisfied with this feature of the usual expositions of measure theory? Is there in fact a good reason for it?

26 September, 2010 at 9:33 pm

Terence TaoAs a general rule in analysis, infinities are only really troublesome in averages, sums, or integrals when they are combined with oscillation, due to the indeterminate nature of the form . But in the non-negative world, they are fairly safe: the laws of algebra for the finite non-negative numbers extend without much distortion to the extended non-negative numbers . So, for the theory of unsigned measures and the unsigned integral, one can extend the theory to allow for infinite measures and infinite integrals without losing any of the main results of the subject (e.g. the monotone convergence theorem works just fine). As a bonus, the number system becomes compact, which manifests itself in a number of useful ways (e.g. infinite series of numbers in always have a limit in ; one no longer has to distinguish between convergent and divergent series).

Once one has oscillation, though, one usually has to minimise the exposure to infinities. For instance, absolutely integrable functions are only allowed to be infinite or undefined on a set of measure zero. Signed measures either have to have finite total variation, or to be infinite on one side only (e.g. infinite positive variation but finite negative variation). And so forth. This is probably the source of the impression you have received from other parts of mathematics that “infinities are dangerous”.

But note also that there are other places in mathematics where infinities can be handled safely, by extending the number system in a suitable fashion. For instance, in complex analysis, poles can often be viewed as being non-singular, by extending the complex plane to the Riemann sphere, and in fact certain aspects of complex geometry (e.g. Mobius transformations) become more natural when doing so. Sometimes, infinity is an ally, rather than an enemy to be feared.

8 November, 2010 at 12:55 pm

Jim RalstonYou could also mention the viewpoint of asymptotic analysis: everything is simpler at infinity; high frequency waves propagate in a simpler way than moderate frequency waves, gravitational fields look like they come from point masses, etc.

[I’ve started reading your website in preparation for Math 245B this winter.]

27 September, 2010 at 4:21 am

JohanIn exercise 50 there is a typo: “on the integers \mathbb{R}”.

[Corrected, thanks – T.]29 September, 2010 at 12:32 am

JohanThe typo has not been fixed completely. It should be \mu(\phi^{-1}(E)), not the other way around.

[Corrected, thanks – T.]27 September, 2010 at 11:53 am

Anonymousdear Prof. Tao,

are you planning to say a few words on ” uniform integrability” which is an important tool in probability theory?

thanks

27 September, 2010 at 12:45 pm

Terence TaoThis will be discussed briefly in the next notes (which will focus on the relationship between different types of convergence).

28 September, 2010 at 1:16 am

JohanExercise 36, the change of variables formula, contains two typos in the definition of the pushforward where the role of \phi and \mu have been interchanged.

[Corrected, thanks – T.]Great series of posts by the way.

28 September, 2010 at 10:48 am

Laurens GunnarsenDear Professor Tao:

Thank you for your thoughtful reply to my earlier question about why abstract measure theory must allow the domain of a measure to include points (i.e., measurable sets) at which it takes on infinite values. Certainly I agree that in various mathematical settings a compelling case can be made for permitting the domain of a function to include points at which it takes on infinite values.

But I would maintain that what what we’re really doing when we take this step is reinterpreting the function’s range. We’re acknowledging that we began with an overly naive conception of the structure we need the range to carry. Mature experience has taught us to value certain structures we did not at first recognize as essential (and to recognize as irrelevant certain other structures we at first thought natural.)

Certainly this is what happens with meromorphic functions. The better we get to know analytic functions with isolated singular points, the more we become convinced that we should think of them as having the Riemann sphere, rather than the complex plane, as their range set. And it is this revision in our conception of their range set’s structure that causes us to alter our views on which points are to be permitted in their domains.

But I would argue that, upon careful reflection, we can see why we benefit by revising our structural conceptions in favor of the Riemann sphere. And it is certainly not just the compactness of the Riemann sphere that makes it a compelling alternative to the complex plane. Rather, the crucial fact is that by adding a point at infinity to the complex plane, we produce a simply-connected compact space to which the conformal structure of the complex plane extends naturally and non-singularly. From this it follows that a finite-dimensional Lie group of conformal automorphisms acts transitively on the Riemann sphere. And it is this crucial structural feature of the Riemann sphere that makes the whole theory of meromorphic functions “work.”

What I meant to ask in my earlier post is how, exactly, the addition of a “point at infinity” alters the structure of the range space of a measure, so as to make the whole of abstract measure theory “work.”

Which is to say that, at bottom, I want to know exactly what structure the range space of a measure must have, if we are to be able to prove all the theorems that we, as the inheritors of generations of profound work and reflection on the abstract theory of measure and its diverse applications, now know that we want and need to be true.

And one thing that, it seems to me, bears strikingly on this question is our experience with spectral measures. There, my impression is that the crucial structure on the range space is that of an orthocomplemented modular lattice (with least and greatest elements.)

Is this your impression as well? Would you be comfortable maintaining that measures must have ranges with at least this much structure, and that the admission of “infinity” as a possible value for the measure of a measurable set is therefore required of us for essentially lattice-theoretic reasons?

Finally, I hope you will forgive me for asking whether you can clarify your remarks concerning the dangers to analysis of the form “infinity” – “infinity,” and the (relative) harmlessness of other forms in which “infinity” enters. Reading your initial remarks on this subject, I was struck immediately by the two examples

S = 1 – 2 + 3 – 4 + 5 – …

and

T = 1 + 2 + 3 + 4 + 4 + … ,

the former of which, though exemplifying the “infinity” – “infinity” form about as obviously as possible, is vastly easier to interpret and handle than the latter (e.g., the former is Borel-, Cesaro-, Abel-, and Euler-summable, whereas the latter is none of these.)

28 September, 2010 at 10:55 am

Terence TaoWell, the main advantage of adding +infinity to the unsigned real axis [0,+infty) to obtain the extended unsigned real axis [0,+\infty] is that infinite series always have a value. For instance, the value of

T = 1 + 2 + 3 + 4 + …

is simply T = +infinity. (This is a different value from those obtained by some other series summation methods, e.g. zeta regularisation, but such summation methods are not relevant for measure theory.) If we excluded infinity from our measure theory, we would always behaving to distinguish between convergent and divergent unsigned infinite sums, but by incorporating infinity into our number system, we can handle both in a unified manner, and one does not lose too many of the laws of algebra when doing so, provided that one is not trying to use cancellation or subtraction. So adding infinity to one’s number system is not just for the sake of maximal generalisation or completion; it can actually simplify and streamline the discussion of the finitary theory.

29 September, 2010 at 5:14 am

studentDear Prof Tao, these are really excellent notes! But for the sake of studying, is there any place I can download the pdf of these lecture notes or any way by which I can generate a pdf of just the post (excluding the side-bars etc.). Thanks.

[The “Print preview” feature on your browser should automatically strip out the sidebar and header from the file. – T.]29 September, 2010 at 6:09 am

AnonymousImmediately following Remark 3, Boolean algebras should be -algebras

[Corrected, thanks – T.]30 September, 2010 at 12:47 pm

AnonymousI think the second instance of L(R^d) in exercise 27 should be B(R^d).

[Corrected, thanks – T.]30 September, 2010 at 1:40 pm

AnonymousShould the scale of D_n(R^d) in Example 6 be 2^-n?

[Corrected, thanks – T.]27 October, 2010 at 10:52 pm

Nick Cookalso, with this correction isn’t D_n+1 now finer than D_n, so the containment goes the other way

[Corrected, thanks – T.]2 October, 2010 at 11:21 am

AnonymousI think the third sentence of section 5 is misworded.

[Corrected, thanks – T.]2 October, 2010 at 3:01 pm

245A, Notes 4: Modes of convergence « What’s new[…] real line with Lebesgue measure. The first three of these examples already were introduced in the previous set of notes. Example 1 (Escape to horizontal infinity) Let . Then converges to zero pointwise (and thus, […]

16 October, 2010 at 8:30 pm

245A, Notes 5: Differentiation theorems « What’s new[…] pointwise almost everywhere to . Applying Fatou’s lemma (Corollary 16 of Notes 3), we conclude […]

30 October, 2010 at 6:54 pm

245A, Notes 6: Outer measures, pre-measures, and product measures « What’s new[…] that contains . In particular, contains all the elementary sets and hence (by Exercise 14 of Notes 3) contains the Borel -algebra. Restricting to the Borel -algebra we obtain the existence […]

4 November, 2010 at 5:56 pm

AnonymousToday a question came up regarding whether the uncountable sum of measures on a measurable space (X,M) was itself a measure. I was a little surprised by the result, but it seems like the answer’s yes:

Let be a measurable space, and be an uncountable collection of measures on . Then is itself a measure on .

Since manifestly, there are two axioms to check:

1. ; and

2. (disjoint) countable additivity: given a disjoint union of measurable sets, .

The first point is trivial: since for all , we must have .

For the second, setting , we consider two cases: first, when is countable, and secondly, when is uncountable.

In the first instance, we are free to apply Tonelli’s Theorem (cf.\ Theorem 2, Notes 1) to find

as desired.

In the second case, decomposing into the countable union

forces some to be uncountable. But then

forcing our desired equality (by the monotonicity of each ):

Make sense?

By the way, this is my first post, so sorry if the formatting doesn’t turn out.

4 November, 2010 at 8:17 pm

XttThis may be a stupid question: In excercise 36, if X and Y are both equipped with trivial algebra, and X has a measure 1 of itself and 0 of empty set. And let the morphism X -> Y be: X -> empty set in Y, and empty set in X -> Y. Does this violate the pushforward is a measure?(since its measure on empty set in Y is not zero)

4 November, 2010 at 8:29 pm

Terence TaoA morphism has to be a function from X to Y. If X is non-empty, then the image of X must also be non-empty; there is no function that can map a non-empty set to an empty set.

In any event, it is the inverse images of the morphism that are more relevant than the forward images, and the inverse image of the empty set by any function is always the empty set (and the inverse image of the range is always the domain).

9 November, 2010 at 6:42 am

cyThe left hand side of the last inequality in the proof of Fatou’s lemma, should be liminf instead of limsup.

[Corrected, thanks – T.]14 November, 2010 at 8:44 pm

YProf. Tao,

For the definition 1, can one drop the first property ? It seems that one can deduce from properties (ii) and (iii). But as you say, it is the “minimalist” definition of a Boolean algebra. Is it because the property ensures that does have “something” that one can not drop it? After all, it seems quite different from the other two properties. Both (ii) and (iii) start with “If…”. Could you explain why (ii) and (iii) are not enough?

14 November, 2010 at 9:08 pm

Terence Tao(ii) and (iii) can only deduce (i) if one knows that the algebra contains at least one set. Otherwise, it could be completely empty (not even containing the empty set: .

The axiom (i) is analogous to the axiom that a group contains an identity element, that a vector space contains an origin, or that an equivalence relation is reflexive. In each of these cases, this basic axiom is almost, but not quite, implied by the other axioms.

28 November, 2010 at 2:26 pm

Erik DavisExcellent notes. There were two small errors I noticed (unless I misread).

In exercise 4, I think you mean , rather than (e.g. the powerset of has cardinality $\latex 2^3$)

In exercise 8, this isn’t true as stated. Consider . Unless our total space is just the singleton , the Boolean algebra generated by will have four elements. For the bound I think you need some extra condition, e.g. the family is a cover for our total space.

[Corrected, thanks. For some strange reason the and got swapped across these two exercises… – T.]19 February, 2015 at 4:16 pm

Dustin BryantActually, if you look at Halmos’ original text, “Lectures on Boolean Algebras” chapter 16 exercise 3, this is 2^{2^n}. So we should consider very carefully who has the typo.

30 November, 2010 at 7:36 pm

AnonymousDear Prof. Tao,

I’m confused in the Exercise 38. What do you mean by when is uncountable?

30 November, 2010 at 7:38 pm

Terence TaoThis is defined in Section 1 of Notes 1.

30 November, 2010 at 8:25 pm

YDear Prof. Tao,

In Exercise 23, you use the notation . Is this a conventional use? Or precisely should it be $latex \sup_{n\in \mathbb{N} } \mu (E_n)}?

[Corrected, thanks – T]30 November, 2010 at 9:41 pm

AnonymousProf. Tao,

I can’t catch up with you in the proof of Monotone convergence theorem. What do you mean by “By horizontal truncation we may assume without loss of generality that g also is finite everywhere”? Could you explain it slightly? What’s the relationship between this statement and the horizontal truncation in Exercise 34.10? How can one get “finite everywhere” just by truncation? In the proof of Theorem 12, you used “horizontal truncation”. By I still don’t understand your idea here.

30 November, 2010 at 9:59 pm

Terence TaoOne uses vertical truncation (applied to g) rather than horizontal truncation here. Once one shows the claim for finite-valued g, the claim then follows for infinite-valued g by replacing g with the vertical truncation , applying the result for finite-valued g, and then letting using Exercise 34.10.

30 November, 2010 at 10:27 pm

AnonymousThank you for your answer. I think the main idea here is the construction of . However, can one just construct without vertically truncate ?

1 December, 2010 at 10:35 am

Terence TaoOne needs to be vertically truncated to ensure that is strictly less than .

29 December, 2015 at 8:28 am

AnonymousSo according to what you changed in Exercise 34.10, you did mean “horizontal truncation” in the proof of MCT, didn’t you? It seems that you are using the phrase “horizontal truncation” with different meanings in mind: 1. truncate the horizontal axis, 2 truncate the graph of a function using a horizontal line.

How time flies :-)

[Corrected to the latter meaning throughout, thanks – T.]12 December, 2010 at 6:57 pm

Damek DavisProfessor Tao,

In Remark 8, it states that $B$ is coarser than $B’$ precisely when, $\text{id}_X : X \rightarrow X$ is a measurable morphism from $(X, B)$ to $(X, B’)$. However, this implies that $B’ \subseteq B$ because every $B’$ measurable set $E$ is $B$ measurable. I think $(X,B)$ and $(X, B’)$ should be switched. Is this correct?

[Corrected, thanks – T.]12 December, 2010 at 8:47 pm

Is the product of measurable spaces the categorical product? « Graduated Understanding[…] respects arbitrary unions and complements, and , we only need to show that and for all and (see remark 4 of Terry Tao’s notes on abstract measure spaces). This is easy to […]

9 June, 2012 at 10:52 pm

RexTypo in Exercise 9: “…all sets that either the union…”

Also, isn’t it the case in Exercise 9 that gives the entire Boolean algebra?

Since we are considering only finite combinations of set operations, it seems that, for any such expression, we could use distributivity to pull all union symbols out of any parentheses containing intersection symbols, and then we would have an expression of the form

9 June, 2012 at 10:59 pm

RexMaybe what I said above was not so clear. What I mean is:

If we write union with the symbol and intersection with the symbol , then for the same reason that we can write any finite combination of sums and products in a ring as a sum of products , we should be able to write any finite combination of set operations as a union over a family of intersections , since we have the same distributivity law that we would have for rings.

10 June, 2012 at 12:58 am

RexTypo: “Exercise 15 (Recursive description of a generated Boolean algebra)” This should read -algebra rather than Boolean algebra, although I notice that this has been corrected in the published book.

Is knowledge of Exercise 15 necessary to prove Exercise 52? I would imagine that one needs some way of describing the sets in in terms of the sets in (such as in Exercise 15) in order to proceed. However, I don’t happen to know anything about ordinals.

I see how to deduce Exercise 52 from the Hahn-Kolmogorov extension theorem, but this feels like cheating.

4 July, 2012 at 4:40 pm

RexDo you have any hints for a way to approach exercise 52?

4 July, 2012 at 10:01 pm

Terence TaoUse Remark 4. (Alternatively: review the proof of the fact that Lebesgue measurable sets of finite measure can be approximated to arbitrary accuracy by elementary sets.)

25 September, 2012 at 2:48 pm

JackFor the construction of integration on abstract measure spaces, the measurable functions of which the range is in or are used. Can we define integration of measurable functions of which the range is or any measurable space? What properties should such space have?

25 September, 2012 at 3:03 pm

Terence TaoTo integrate functions taking values in a finite-dimensional vector space, one can pick a basis for that vector space and integrate each coordinate of the vector-valued function separately; this gives a well-defined notion of integral that is independent of the choice of basis, and the theory is more or less the same as that for scalar integration.

For functions taking values in infinite-dimensional vector spaces, things get trickier (much as infinite sums become a more subtle topic in infinite-dimensional vector spaces than in finite-dimensional ones, due to the number of different concepts of convergence for such sums). The two most popular notions of integral here are the Bochner integral and the Pettis integral, with the latter being more general (but weaker) than the former. But one can make mistakes if one blindly assumes that all the theorems of Lebesgue integration carry over verbatim to this infinite dimensional setting, so I would recommend working out any particular manipulation (e.g. interchanging limits and integrals) for such vector-valued integrals carefully by hand whenever the need arises.

For functions taking values in a nonlinear space (e.g. an abstract manifold), the notion of even a finite sum becomes problematic, let alone infinite sums or integrals. It is still worthwhile sometimes to attempt to define integrals or averages on nonlinear spaces, but this generally has to be done on an

ad hocbasis (there is no useful general theory analogous to the Lebesgue integration theory here), and it is not always guaranteed that a good integration concept actually exists.26 February, 2013 at 7:44 am

JackThe convergence theorems basically deal with questions that when we have

. Is there any theorem in real analysis about the “continuous version” of this formula? That is when can we have for instance something like

?

27 February, 2013 at 3:30 pm

Terence TaoYes, either by adapting the proofs of the sequential convergence theorems, or by recasting the notion of continuous convergence in terms of sequential convegence (e.g. can be reformulated as the assertion that whenever ).

24 March, 2013 at 4:41 am

JackThe change of variables formula in Riemann integration is

.

[From http://www.math.ucla.edu/~tao/resource/general/131ah.1.03w/week10.pdf%5D

In Exercise 36,

Should they relate to each other since you call them both the change of variables formula?

Let , , and $\phi$ be the measurable morphism. The first one is almost the same as the second one. But the measure doesn’t match. What do I do wrong here…

26 March, 2013 at 6:11 pm

JackLet and $Y=[a,b]$. Then the first one can be written as

Do you have any hint for the last step, i.e.

?

25 June, 2013 at 11:16 pm

Luqing YeA remark to Exercise 14:

This exercise make use of the essential facts that:

1.A countable intersection of Borel measurable sets is also Borel measurable.

2.Every closed set in can be represented as a countable intersection of open sets.

3.Every open set in can be represented as a countable union open closed intervals.

26 June, 2013 at 12:11 am

Luqing YeDear Prof.Tao,

I think “Exercise 15 (Recursive description of a generated Boolean algebra)” should be “Exercise 15 (Recursive description of a generated algebra) ”…

29 June, 2013 at 8:44 pm

Solutions to Exercise 9 and Exercise 15 of 245A, Notes 3: Integration on abstract measure spaces, and the convergence theorems | Asymptote[…] In this post I give solutions to Exercise 9 and Exercise 15 of Terence Tao’s post 245A, Notes 3: Integration on abstract measure spaces, and the convergence theorems. […]

30 June, 2013 at 7:59 pm

Solution to Exercise 16 of 245A, Notes 3: Integration on abstract measure spaces, and the convergence theorems | Asymptote[…] In this post I give solution to Exercise 16 of 245A, Notes 3: Integration on abstract measure spaces, and the convergence theorems […]

1 July, 2013 at 2:03 am

Luqing YeA remark to Exercise 18:

If is Borel unmeasurable,then is also Borel unmeasurable in .

This is a difference between the Borel measure and Lebesgue measure.

3 July, 2013 at 5:34 am

Luqing YeDear Prof.Tao and peers,

Definition 8 is stated as follows:

Let be a measurable space, and let or be an unsigned or complex-valued function. We say that is measurable if is -measurable for every open subset of .

Why not stated as follows:

Let be a measurable space, and let or be an unsigned or complex-valued function. We say that is measurable if is -measurable for every open subset of .

3 July, 2013 at 9:41 pm

Terence TaoOne can define measurable maps between any two measurable spaces, but the reason I restrict here to or to is because these two spaces have well-defined addition and multiplication operations, so that one can integrate simple functions without difficulty. For or , addition and multiplication are not always defined. (This is not a serious issue if the functions involved are finite almost everywhere, but it is best to not introduce these issues right away as they are not the most important detail in integration theory.)

7 July, 2013 at 6:56 pm

Solution to Exercise 28.6 of 245A, Notes 3: Integration on abstract measure spaces, and the convergence theorems | Asymptote[…] In this post I give solution to Exercise 28.6 of 245A, Notes 3: Integration on abstract measure spaces, and the convergence theorems. […]

8 July, 2013 at 10:53 pm

Solution to Exercise 28.8 of 245A, Notes 3: Integration on abstract measure spaces, and the convergence theorems | Asymptote[…] In this post I give solution to Exercise 28.8 of 245A, Notes 3: Integration on abstract measure spaces, and the convergence theorems. […]

9 July, 2013 at 6:26 am

Luqing YeA remark to Remark 8:

This conclusion in Remark 8 is insightful:“Also, one -algebra on a space is coarser than another precisely when the identity map is a measurable morphism from to .”

It makes me think deeply.

1.If is continuous,then will transform any open sets in into an open sets in ,so informally speaking, a continuous map is no better than an identity map,because the inverse of an identity map can also transform any open set in into an open set.This indicates that a continuous map is too regular,too normal that it is very similar to the identity map!

Let’s say it in another way,if is a continuous map,then maps a algebra on space to another algebra on ,neither of the two algebra is coarser than the other one.

2.If is Lebesgue measurable,then will transform any open sets in into a Lebesgue measurable set in ,so informally speaking,a Lebesgue measurable function is better than an identity map,this indicates that a Lebesgue measurable function has higher level of complexity than the identity function and continuous function.

Let’s say it in another way,if is a L-measurable map,then maps a algebra on space to another algebra on ,and is coarser than .

13 July, 2013 at 3:08 am

Luqing YeOnly unitil the day before yesterday I discovered that I had a wrong proof of Egorov’s theorem before.Now I prove it again!I think this time my proof will be right ,and be a bit shorter than Mr.Tao’s proof.

Assume the condition stated in this Exercise 30 (Egorov’s theorem).

is pointwise approximated by ,where is a function, are measurable function.

This means that,,we have

By applying the monotone convergence theorem to (1)(Note is finite measure is a must have condition),together with the equation

We have

Translating (3) into English,we have the conclusion that can be uniformly approximated by on ,where is a measurable set whose measure is at most .

13 July, 2013 at 6:19 am

Luqing YeI am wrong again.The corrected version is here,now I believe I will be right:

is pointwise approximated by ,where is a function, are measurable function.

This means that,,we have

By applying the monotone convergence theorem to (1)(Note is finite measure is a must have condition),together with the equation

We have

Translating (3) into English,we have the conclusion that can be uniformly approximated by on ,where is a measurable set whose measure is very small.

13 July, 2013 at 6:52 am

Luqing YeI wonder why there is no definition of outer measure in general measure space.I think the outer measure of can be defined as “the measure of the smallest measurable set that covers “.

13 July, 2013 at 6:08 pm

A proof of Egorov’s theorem | Asymptote[…] In this post I give a proof of Egorov’s theorem stated in Exercise 30 of Terence Tao’s post 245A, Notes 3: Integration on abstract measure spaces, and the convergence theorems. […]

15 July, 2013 at 5:18 pm

A proof of inclusion-exclusion principle | Asymptote[…] In this post,I prove inclusion-exclusion principle inductively.This principle can be found at Exercise 33 in Terence Tao’s post 245A, Notes 3: Integration on abstract measure spaces, and the convergence theorems. […]

15 July, 2013 at 5:59 pm

Luqing YeA remark to Exercise 28,(6):

Exercise 28.6 says:If are a sequence of measurable functions that converge pointwise to a limit , then show that is also measurable. Obtain the same claim if is replaced by .

I can do this exercise,but I wonder whether the following is true?

A measurable function can be pointwise approximated by a sequence of simple functions.

I can’t prove it,so I believe this is not true.

15 July, 2013 at 6:58 pm

Luqing YeA remark to definition 11:

Definition 11 says: Let be a measure space, and let be measurable. Then we define the unsigned integral of by the formula

I wonder why not define the unsigned integral as follows?

16 July, 2013 at 6:53 am

Luqing YeDear Prof.Tao,

Exercise 34,(9) says:

(Vertical truncation) We have .

But I think an addtional hypothesis should be added: should be finite almost everywhere.

16 July, 2013 at 7:02 am

Luqing YeOh,I am wrong….This additional hypothesis is not needed…

25 July, 2013 at 4:21 am

Luqing YeA remark to Theorem 17 (Dominated convergence theorem):

I proved the dominated convergence theorem using Egorove’s theorem together with Exercise 40 (Uniform convergence on a finite measure space) when I was walking outside home.

I don’t use Fatou’s lemma…..BTW,I think Fatou’s lemma is a naive and easy stuff,so I will be comfortable that I don’t use this lemma….

25 July, 2013 at 9:16 pm

Luqing YeDear Prof.Tao,

In the proof of Theorem 17 (Dominated convergence theorem),I think the last inequality

should be

25 July, 2013 at 9:25 pm

Luqing YeI am wrong again……Maybe next time I shouldn’t put any comment until I think 10 times.

26 July, 2013 at 6:50 am

A proof of Fatou’s lemma « Asymptote[…] In this post I give a proof of Fatou’s lemma which is the Corollary 16 of Terence Tao’s post 245A, Notes 3: Integration on abstract measure spaces, and the convergence theorems. […]

28 July, 2013 at 7:03 am

Luqing YeDear Prof.Tao,

Could you please give me some hints on how the condition “Suppose that is complete, which means that every sub-null set is a null set.” is used in Exercise 49?

I have a bit doubt on my ability to give a correct proof because I proved Exercise 49 without using this condition……

28 July, 2013 at 8:38 am

Terence TaoIf is an indicator function of a sub-null set, what are the upper and lower integrals of ?

28 July, 2013 at 8:38 pm

Luqing YeIf is an indicator function of an unmeasurable sub-null set,it seems that the upper and lower integrals of does not exist.

Dear Prof.Tao,

Thank you for your hint.Now I understand that I made a skip in my proof,I thought that according to Littlewood’s principle,I can ignore the subnull set and null set but actually I should not because if I ignore the unmeasurable subnull set,then I will get an unmeasurable monster.

The reason that I proved this exercise wrongly is that I used wrong intuition.I carry my intuition in Lebesgue measure into this general measure space setting,which result in my fault.More precisely,it seems that Littlewood’s three principle should only work in complete measure space,otherwise I will get a monster……

4 July, 2016 at 6:15 pm

Suntingi think the important aspect is that if X is not complete, then if fn(measurable) converges to f, a.e., we could not say that f is measurable. if it is complete, then f is measurable.if f is an indicator function of subnull set, then upper and lower integrals both are zero, so they agree, but it seems that in this case f is not measurable.

1 September, 2014 at 4:41 pm

AnonymousAre Borel sigma algebra and the same? In Folland’s Real Analysis, the author uses to show that a complex function is measurable iff both the real and imaginary parts are measurable.

and have different structures. Why one can identify the Borel sigma algebras on them?

1 September, 2014 at 5:13 pm

Terence Taoand have the same topology (after identifying the sets together in the usual fashion), and hence the same Borel sigma-algebra.

1 September, 2014 at 5:36 pm

AnonymousWhen you say “identifying”, do you mean “homeomorphism”? In one has multiplication for . But in , one only has inner product , which is not the same as the previous one. Is it because one discards such structures and considers “topology” only that one can “identify” these two Borel sigma algebra. As I understand from your comment, should one view “=” in the identity above as “homromorphism” (or some sort of “isomorphism”)?

1 September, 2014 at 6:51 pm

Terence TaoThe standard identification between and is an isomorphism of sets (otherwise known as a bijection), and is furthermore an isomorphism of topological spaces (otherwise known as a homeomorphism). As you noted, though, it is not an isomorphism of rings or of inner product spaces. (It is an isomorphism of (additive) groups, but this is not particularly relevant for the current discussion.)

19 September, 2014 at 12:56 pm

AnonymousI don’t follow the “without loss of generality” part of the proof of MCT. Suppose is not finite every where, say, and is not of measure zero. How to go on in this situation? How does “vertical truncation” help here (I don’t know what is “truncated” here…)?

19 September, 2014 at 2:20 pm

Terence TaoIf g attains infinite values, replace g with (this is the “vertical truncation” of g at height N) and then let .

24 May, 2015 at 7:20 am

AlexDo you have any additional hint on how to prove the dominated convergence theorem for sets? Following the given hint, one takes

and by monotone convergence for sets

How can I prove that ?

Thank you!

[You don’t need to prove to finish the problem. One just needs an appropriate inequality relating with and . -T.]29 September, 2015 at 9:54 pm

275A, Notes 0: Foundations of probability theory | What's new[…] begin with the notion of a measurable space. (See also this previous blog post, which covers similar material from the perspective of a real analysis graduate class rather than a […]

2 October, 2015 at 10:11 pm

TECNOLOGÍA » 275A, Notes 0: Foundations of probability theory[…] begin with the notion of a measurable space. (See also this previous blog post, which covers similar material from the perspective of a real analysis graduate class rather than a […]

3 October, 2015 at 2:58 pm

275A, Notes 1: Integration and expectation | What's new[…] of the details of this construction will be left to exercises. See also Chapter 1 of Durrett, or these previous blog notes, for a more detailed […]

16 December, 2015 at 4:22 pm

AnonymousIn the proof of MCT, why we need the there? What could fail if we just define

the sets

?

16 December, 2015 at 4:26 pm

AnonymousI meant

.

16 December, 2015 at 4:37 pm

Terence TaoIn this case, the need not increase to . (For instance, one could have on .)

17 December, 2015 at 10:55 am

AnonymousThe reader is encouraged to see why, in each of the moving bump examples, no such dominating function exists, without appealing to the above theorem (DCT)To dominate

would be such that for almost everywhere, which can not be integrable. Do you have a hint why there is no integrable dominant function for

17 December, 2015 at 12:47 pm

Terence TaoGraph the functions , and compare against the graph of .

1 August, 2016 at 7:02 am

Joe LiSmall typo in Exercise 31. should be .

[Corrected, thanks – T.]22 November, 2016 at 7:25 am

coupon_clipperSorry for the simple question, but can you give a hint for Exercise 34 part 10 (vertical truncation)? I feel like none of the old tricks from Lebesgue integration work here, and we don’t even know if the union of the have finite measure. Every time I try to write out a proof, I end up trying to switch limits around, which is what I’m trying to prove I can do.

[First try the case when is a simple function, or even an indicator function. -T.]23 November, 2016 at 2:43 pm

Coupon_clipperThanks Terry. Got it now. Have a great Thanksgiving!

12 March, 2017 at 11:08 pm

IanConcerning exercise 18, part 1: Is this statement true? How can one prove that if E has measurable projection, then its complement does too? (since projection does not commute with taking complements)

[Exercise 18 concerns slices, not projections. -T]16 January, 2019 at 10:57 am

245A: Problem solving strategies | What's new[…] the generating sets; in some cases one can also take balls, cubes, or rectangles); see Remark 4 of Notes 3. One can relax the set of properties required, for instance countable intersections is […]