## Description

EECS 126: Probability and Random Processes

Problem Set 10

1. Random Graph Estimation

Consider a random graph on n vertices in which each edge appears independently with

probability p. Let D be the average degree of a vertex in the graph. Compute the maximum

likelihood estimator of p given D. You may approximate Binomial(n, p) = Poisson(np).

2. Introduction to Information Theory

Recall that the entropy of a discrete random variable X is defined as

H(X)

∆= −

X

x

p(x) log p(x) = −E[log p(X)],

where p(·) is the PMF of X. Here, the logarithm is taken with base 2, and entropy is measured

in bits.

(a) Prove that H(X) ≥ 0.

(b) Entropy is often described as the average information content of a random variable. If

H(X) = 0, then no new information is given by observing X. On the other hand, if

H(X) = m, then observing the value of X gives you m bits of information on average.

Let X be a Bernoulli random variable with P(X = 1) = p. Would you expect H(X) to

be greater when p = 1/2 or when p = 1/3? Calculate H(X) in both of these cases and

verify your answer.

(c) We now consider a binary erasure channel (BEC).

Figure 1: The channel model for the BEC showing a mapping from channel input X to channel

output Y . The probability of erasure is pe.

The input X is a Bernoulli random variable with P(X = 0) = P(X = 1) = 1/2. Each

time that we use the channel the input X will either get get erased with probability pe,

or it will get transmitted correctly with probability 1 − pe. Using the character “?” to

denote erasures, the output Y of the channel can be written as

Y =

(

X, with probability 1 − pe

?, with probability pe.

Compute H(Y ).

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(d) We defined the entropy of a single random variable as a measure of the uncertainty

inherent in the distribution of the random variable. We now extend this definition for a

pair of random variables (X, Y ), but there is nothing really new in this definition because

the pair (X, Y ) can be considered to be a single vector-valued random variable. Define

the joint entropy of a pair of discrete random variables (X, Y ) to be

H(X, Y )

∆= −E[log p(X, Y )],

where p(·, ·) is the joint PMF and the expectation is also taken over the joint distribution

of X and Y .

Compute H(X, Y ), for the BEC.

3. Info Theory Bounds

In this problem we explore some intuitive results which can be formalized using information

theory.

(a) (optional) Prove Jensen’s inequality: if f is a convex function and Z is random variable,

then f(E[Z]) ≤ E[f(Z)]. Hint: You can use fact that every convex function can be

represented by the pointwise supremum of affine functions that are bounded above by f,

i.e.

f(x) = sup{l(x) = ax + b : l(x) ≤ f(x) ∀x}.

(b) It turns out that there is actually a limit to how much “randomness” there is in a

random variable X which takes on |X | distinct values. Show that for any distribution

pX, H(X) ≤ log |X |. Use this to conclude that if a random variable X takes values in

[n] := {1, 2, . . . , n}, then the distribution which maximizes H(X) is X ∼ Uniform([n]).

(c) For two random variable X, Y we define the mutual information (this should have also

been covered in discussion) to be

I(X; Y ) = X

x

X

y

p(x, y) log p(x, y)

p(x)p(y)

,

where the sums are taken over all outcomes of X and Y . Show that I(X; Y ) ≥ 0. In

discussion, you have seen that I(X; Y ) = H(X) − H(X|Y ). Therefore the fact that

mutual information is nonnegative means intuitively that conditioning will only ever

reduce our uncertainty.

4. Relative Entropy and Stationary Distributions

We define the relative entropy, also known as Kullback-Leibler divergence, between two

distributions p and q as

D(p||q) = EX∼p

log

p(X)

q(X)

=

X

x

p(x) log p(x)

q(x)

(a) Show that D(p||q) ≥ 0, with equality if and only if p(x) = q(x) for all x. Thus, it is

useful to think about D(·||·) as a sort of distance function. Hint: For strictly concave

functions f, Jensen’s inequality states that f(E[Z]) ≥ E[f(Z)] with equality if and only

if Z is constant.

(b) Show that for any irreducible Markov chain with stationary distribution π, any other

stationary distribution µ must be equal to π. Hint: Consider D(π||µP).

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