Let ${X}$ and ${Y}$ be two random variables taking values in the same (discrete) range ${R}$, and let ${E}$ be some subset of ${R}$, which we think of as the set of “bad” outcomes for either ${X}$ or ${Y}$. If ${X}$ and ${Y}$ have the same probability distribution, then clearly

$\displaystyle {\bf P}( X \in E ) = {\bf P}( Y \in E ).$

In particular, if it is rare for ${Y}$ to lie in ${E}$, then it is also rare for ${X}$ to lie in ${E}$.

If ${X}$ and ${Y}$ do not have exactly the same probability distribution, but their probability distributions are close to each other in some sense, then we can expect to have an approximate version of the above statement. For instance, from the definition of the total variation distance ${\delta(X,Y)}$ between two random variables (or more precisely, the total variation distance between the probability distributions of two random variables), we see that

$\displaystyle {\bf P}(Y \in E) - \delta(X,Y) \leq {\bf P}(X \in E) \leq {\bf P}(Y \in E) + \delta(X,Y) \ \ \ \ \ (1)$

for any ${E \subset R}$. In particular, if it is rare for ${Y}$ to lie in ${E}$, and ${X,Y}$ are close in total variation, then it is also rare for ${X}$ to lie in ${E}$.

A basic inequality in information theory is Pinsker’s inequality

$\displaystyle \delta(X,Y) \leq \sqrt{\frac{1}{2} D_{KL}(X||Y)}$

where the Kullback-Leibler divergence ${D_{KL}(X||Y)}$ is defined by the formula

$\displaystyle D_{KL}(X||Y) = \sum_{x \in R} {\bf P}( X=x ) \log \frac{{\bf P}(X=x)}{{\bf P}(Y=x)}.$

(See this previous blog post for a proof of this inequality.) A standard application of Jensen’s inequality reveals that ${D_{KL}(X||Y)}$ is non-negative (Gibbs’ inequality), and vanishes if and only if ${X}$, ${Y}$ have the same distribution; thus one can think of ${D_{KL}(X||Y)}$ as a measure of how close the distributions of ${X}$ and ${Y}$ are to each other, although one should caution that this is not a symmetric notion of distance, as ${D_{KL}(X||Y) \neq D_{KL}(Y||X)}$ in general. Inserting Pinsker’s inequality into (1), we see for instance that

$\displaystyle {\bf P}(X \in E) \leq {\bf P}(Y \in E) + \sqrt{\frac{1}{2} D_{KL}(X||Y)}.$

Thus, if ${X}$ is close to ${Y}$ in the Kullback-Leibler sense, and it is rare for ${Y}$ to lie in ${E}$, then it is rare for ${X}$ to lie in ${E}$ as well.

We can specialise this inequality to the case when ${Y}$ a uniform random variable ${U}$ on a finite range ${R}$ of some cardinality ${N}$, in which case the Kullback-Leibler divergence ${D_{KL}(X||U)}$ simplifies to

$\displaystyle D_{KL}(X||U) = \log N - {\bf H}(X)$

where

$\displaystyle {\bf H}(X) := \sum_{x \in R} {\bf P}(X=x) \log \frac{1}{{\bf P}(X=x)}$

is the Shannon entropy of ${X}$. Again, a routine application of Jensen’s inequality shows that ${{\bf H}(X) \leq \log N}$, with equality if and only if ${X}$ is uniformly distributed on ${R}$. The above inequality then becomes

$\displaystyle {\bf P}(X \in E) \leq {\bf P}(U \in E) + \sqrt{\frac{1}{2}(\log N - {\bf H}(X))}. \ \ \ \ \ (2)$

Thus, if ${E}$ is a small fraction of ${R}$ (so that it is rare for ${U}$ to lie in ${E}$), and the entropy of ${X}$ is very close to the maximum possible value of ${\log N}$, then it is rare for ${X}$ to lie in ${E}$ also.

The inequality (2) is only useful when the entropy ${{\bf H}(X)}$ is close to ${\log N}$ in the sense that ${{\bf H}(X) = \log N - O(1)}$, otherwise the bound is worse than the trivial bound of ${{\bf P}(X \in E) \leq 1}$. In my recent paper on the Chowla and Elliott conjectures, I ended up using a variant of (2) which was still non-trivial when the entropy ${{\bf H}(X)}$ was allowed to be smaller than ${\log N - O(1)}$. More precisely, I used the following simple inequality, which is implicit in the arguments of that paper but which I would like to make more explicit in this post:

Lemma 1 (Pinsker-type inequality) Let ${X}$ be a random variable taking values in a finite range ${R}$ of cardinality ${N}$, let ${U}$ be a uniformly distributed random variable in ${R}$, and let ${E}$ be a subset of ${R}$. Then

$\displaystyle {\bf P}(X \in E) \leq \frac{(\log N - {\bf H}(X)) + \log 2}{\log 1/{\bf P}(U \in E)}.$

Proof: Consider the conditional entropy ${{\bf H}(X | 1_{X \in E} )}$. On the one hand, we have

$\displaystyle {\bf H}(X | 1_{X \in E} ) = {\bf H}(X, 1_{X \in E}) - {\bf H}(1_{X \in E} )$

$\displaystyle = {\bf H}(X) - {\bf H}(1_{X \in E})$

$\displaystyle \geq {\bf H}(X) - \log 2$

by Jensen’s inequality. On the other hand, one has

$\displaystyle {\bf H}(X | 1_{X \in E} ) = {\bf P}(X \in E) {\bf H}(X | X \in E )$

$\displaystyle + (1-{\bf P}(X \in E)) {\bf H}(X | X \not \in E)$

$\displaystyle \leq {\bf P}(X \in E) \log |E| + (1-{\bf P}(X \in E)) \log N$

$\displaystyle = \log N - {\bf P}(X \in E) \log \frac{N}{|E|}$

$\displaystyle = \log N - {\bf P}(X \in E) \log \frac{1}{{\bf P}(U \in E)},$

where we have again used Jensen’s inequality. Putting the two inequalities together, we obtain the claim. $\Box$

Remark 2 As noted in comments, this inequality can be viewed as a special case of the more general inequality

$\displaystyle {\bf P}(X \in E) \leq \frac{D(X||Y) + \log 2}{\log 1/{\bf P}(Y \in E)}$

for arbitrary random variables ${X,Y}$ taking values in the same discrete range ${R}$, which follows from the data processing inequality

$\displaystyle D( f(X)||f(Y)) \leq D(X|| Y)$

for arbitrary functions ${f}$, applied to the indicator function ${f = 1_E}$. Indeed one has

$\displaystyle D( 1_E(X) || 1_E(Y) ) = {\bf P}(X \in E) \log \frac{{\bf P}(X \in E)}{{\bf P}(Y \in E)}$

$\displaystyle + {\bf P}(X \not \in E) \log \frac{{\bf P}(X \not \in E)}{{\bf P}(Y \not \in E)}$

$\displaystyle \geq {\bf P}(X \in E) \log \frac{1}{{\bf P}(Y \in E)} - h( {\bf P}(X \in E) )$

$\displaystyle \geq {\bf P}(X \in E) \log \frac{1}{{\bf P}(Y \in E)} - \log 2$

where ${h(u) := u \log \frac{1}{u} + (1-u) \log \frac{1}{1-u}}$ is the entropy function.

Thus, for instance, if one has

$\displaystyle {\bf H}(X) \geq \log N - o(K)$

and

$\displaystyle {\bf P}(U \in E) \leq \exp( - K )$

for some ${K}$ much larger than ${1}$ (so that ${1/K = o(1)}$), then

$\displaystyle {\bf P}(X \in E) = o(1).$

More informally: if the entropy of ${X}$ is somewhat close to the maximum possible value of ${\log N}$, and it is exponentially rare for a uniform variable to lie in ${E}$, then it is still somewhat rare for ${X}$ to lie in ${E}$. The estimate given is close to sharp in this regime, as can be seen by calculating the entropy of a random variable ${X}$ which is uniformly distributed inside a small set ${E}$ with some probability ${p}$ and uniformly distributed outside of ${E}$ with probability ${1-p}$, for some parameter ${0 \leq p \leq 1}$.

It turns out that the above lemma combines well with concentration of measure estimates; in my paper, I used one of the simplest such estimates, namely Hoeffding’s inequality, but there are of course many other estimates of this type (see e.g. this previous blog post for some others). Roughly speaking, concentration of measure inequalities allow one to make approximations such as

$\displaystyle F(U) \approx {\bf E} F(U)$

with exponentially high probability, where ${U}$ is a uniform distribution and ${F}$ is some reasonable function of ${U}$. Combining this with the above lemma, we can then obtain approximations of the form

$\displaystyle F(X) \approx {\bf E} F(U) \ \ \ \ \ (3)$

with somewhat high probability, if the entropy of ${X}$ is somewhat close to maximum. This observation, combined with an “entropy decrement argument” that allowed one to arrive at a situation in which the relevant random variable ${X}$ did have a near-maximum entropy, is the key new idea in my recent paper; for instance, one can use the approximation (3) to obtain an approximation of the form

$\displaystyle \sum_{j=1}^H \sum_{p \in {\mathcal P}} \lambda(n+j) \lambda(n+j+p) 1_{p|n+j}$

$\displaystyle \approx \sum_{j=1}^H \sum_{p \in {\mathcal P}} \frac{\lambda(n+j) \lambda(n+j+p)}{p}$

for “most” choices of ${n}$ and a suitable choice of ${H}$ (with the latter being provided by the entropy decrement argument). The left-hand side is tied to Chowla-type sums such as ${\sum_{n \leq x} \frac{\lambda(n)\lambda(n+1)}{n}}$ through the multiplicativity of ${\lambda}$, while the right-hand side, being a linear correlation involving two parameters ${j,p}$ rather than just one, has “finite complexity” and can be treated by existing techniques such as the Hardy-Littlewood circle method. One could hope that one could similarly use approximations such as (3) in other problems in analytic number theory or combinatorics.