Mutual information for continuous random variables *

y

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

p(x)p(y) (2.119)

We have I(X;Y)≥0 with equality iff p(X, Y) =p(X)p(Y). That is, the MI is zero iff the variables are independent.

To gain insight into the meaning of MI, it helps to re-express it in terms of joint and conditional entropies. One can show (Exercise 2.12) that the above expression is equivalent to the following:

I(X;Y) =H(X)−H(X|Y) =H(Y)−H(Y|X) (2.120)

where H(Y|X) is the conditional entropy, defined as H(Y|X) =

xp(x)H(Y|X =x).

Thus we can interpret the MI betweenX and Y as the reduction in uncertainty aboutX after observingY, or, by symmetry, the reduction in uncertainty aboutY after observingX. We will encounter several applications of MI later in the book. See also Exercises 2.13 and 2.14 for the connection between MI and correlation coefficients.

A quantity which is closely related to MI is the pointwise mutual informationor PMI. For two events (not random variables)xandy, this is defined as

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

p(x)p(y) = logp(x|y)

p(x) = logp(y|x)

p(y) (2.121)

This measures the discrepancy between these events occuring together compared to what would be expected by chance. Clearly the MI of X and Y is just the expected value of the PMI.

Interestingly, we can rewrite the PMI as follows:

PMI(x, y) = logp(x|y)

p(x) = logp(y|x)

p(y) (2.122)

This is the amount we learn from updating the prior p(x)into the posteriorp(x|y), or equiva-lently, updating the priorp(y)into the posteriorp(y|x).

2.8.3.1 Mutual information for continuous random variables *

The above formula for MI is defined for discrete random variables. For continuous random variables, it is common to first discretize or quantize them, by dividing the ranges of each variable into bins, and computing how many values fall in each histogram bin (Scott 1979). We can then easily compute the MI using the formula above (seemutualInfoAllPairsMixed for some code, andmiMixedDemofor a demo).

Unfortunately, the number of bins used, and the location of the bin boundaries, can have a significant effect on the results. One way around this is to try to estimate the MI directly,

Figure 2.22 Left: Correlation coefficient vs maximal information criterion (MIC) for all pairwise relation-ships in the WHO data. Right: scatter plots of certain pairs of variables. The red lines are non-parametric smoothing regressions (Section 15.4.6) fit separately to each trend. Source: Figure 4 of (Reshed et al. 2011) . Used with kind permission of David Reshef and the American Association for the Advancement of Science.

without first performing density estimation (Learned-Miller 2004). Another approach is to try many different bin sizes and locations, and to compute the maximum MI achieved. This statistic, appropriately normalized, is known as the maximal information coefficient (MIC) (Reshed et al. 2011). More precisely, define

m(x, y) =max_G∈G(x,y)I(X(G);Y(G))

log min(x, y) (2.123)

whereG(x, y)is the set of 2d grids of sizex×y, andX(G),Y(G)represents a discretization of the variables onto this grid. (The maximization over bin locations can be performed efficiently using dynamic programming (Reshed et al. 2011).) Now define the MIC as

MIC max

x,y:xy<Bm(x, y) (2.124)

where B is some sample-size dependent bound on the number of bins we can use and still reliably estimate the distribution ((Reshed et al. 2011) suggestB =N^0.6). It can be shown that the MIC lies in the range[0,1], where 0 represents no relationship between the variables, and 1 represents a noise-free relationship of any form, not just linear.

Figure 2.22 gives an example of this statistic in action. The data consists of 357 variables measuring a variety of social, economic, health and political indicators, collected by the World Health Organization (WHO). On the left of the figure, we see the correlation coefficient (CC) plotted against the MIC for all 63,566 variable pairs. On the right of the figure, we see scatter plots for particular pairs of variables, which we now discuss:

• The point marked C has a low CC and a low MIC. The corresponding scatter plot makes it

clear that there is no relationship between these two variables (percentage of lives lost to injury and density of dentists in the population).

• The points marked D and H have high CC (in absolute value) and high MIC, because they represent nearly linear relationships.

• The points marked E, F, and G have low CC but high MIC. This is because they correspond to non-linear (and sometimes, as in the case of E and F, non-functional, i.e., one-to-many) relationships between the variables.

In summary, we see that statistics (such as MIC) based on mutual information can be used to discover interesting relationships between variables in a way that simpler measures, such as correlation coefficients, cannot. For this reason, the MIC has been called “a correlation for the 21st century” (Speed 2011).

Exercises

Exercise 2.1Probabilities are sensitive to the form of the question that was used to generate the answer (Source: Minka.) My neighbor has two children. Assuming that the gender of a child is like a coin flip, it is most likely, a priori, that my neighbor has one boy and one girl, with probability 1/2. The other possibilities—two boys or two girls—have probabilities 1/4 and 1/4.

a. Suppose I ask him whether he has any boys, and he says yes. What is the probability that one child is a girl?

b. Suppose instead that I happen to see one of his children run by, and it is a boy. What is the probability that the other child is a girl?

Exercise 2.2Legal reasoning

(Source: Peter Lee.) Suppose a crime has been committed. Blood is found at the scene for which there is no innocent explanation. It is of a type which is present in 1% of the population.

a. The prosecutor claims: “There is a 1% chance that the defendant would have the crime blood type if he were innocent. Thus there is a 99% chance that he guilty”. This is known as theprosecutor’s fallacy.

What is wrong with this argument?

b. The defender claims: “The crime occurred in a city of 800,000 people. The blood type would be found in approximately 8000 people. The evidence has provided a probability of just 1 in 8000 that the defendant is guilty, and thus has no relevance.” This is known as thedefender’s fallacy. What is wrong with this argument?

Exercise 2.3Variance of a sum

Show that the variance of a sum isvar [X+Y] = var [X] + var [Y] + 2cov [X, Y],wherecov [X, Y] is the covariance betweenX andY

Exercise 2.4Bayes rule for medical diagnosis

(Source: Koller.) After your yearly checkup, the doctor has bad news and good news. The bad news is that you tested positive for a serious disease, and that the test is 99% accurate (i.e., the probability of testing positive given that you have the disease is 0.99, as is the probability of tetsing negative given that you don’t have the disease). The good news is that this is a rare disease, striking only one in 10,000 people. What are the chances that you actually have the disease? (Show your calculations as well as giving the final result.)

Exercise 2.5The Monty Hall problem

(Source: Mackay.) On a game show, a contestant is told the rules as follows:

There are three doors, labelled 1, 2, 3. A single prize has been hidden behind one of them. You get to select one door. Initially your chosen door willnotbe opened. Instead, the gameshow host will open one of the other two doors, andhe will do so in such a way as not to reveal the prize.For example, if you first choose door 1, he will then open one of doors 2 and 3, and it is guaranteed that he will choose which one to open so that the prize will not be revealed.

At this point, you will be given a fresh choice of door: you can either stick with your first choice, or you can switch to the other closed door. All the doors will then be opened and you will receive whatever is behind your final choice of door.

Imagine that the contestant chooses door 1 first; then the gameshow host opens door 3, revealing nothing behind the door, as promised. Should the contestant (a) stick with door 1, or (b) switch to door 2, or (c) does it make no difference? You may assume that initially, the prize is equally likely to be behind any of the 3 doors. Hint: use Bayes rule.

Exercise 2.6Conditional independence (Source: Koller.)

a. LetH∈ {1, . . . , K}be a discrete random variable, and lete₁ ande₂ be the observed values of two other random variablesE₁ andE₂. Suppose we wish to calculate the vector

P(H|e₁, e₂) = (P(H = 1|e₁, e₂), . . . , P(H=K|e₁, e₂)) Which of the following sets of numbers are sufficient for the calculation?

i. P(e₁, e₂),P(H),P(e₁|H),P(e₂|H) ii. P(e₁, e₂),P(H),P(e₁, e₂|H) iii. P(e₁|H),P(e₂|H),P(H)

b. Now suppose we now assumeE₁ ⊥E₂|H (i.e.,E₁ andE₂ are conditionally independent given H).

Which of the above 3 sets are sufficent now?

Show your calculations as well as giving the final result. Hint: use Bayes rule.

Exercise 2.7Pairwise independence does not imply mutual independence We say that two random variables are pairwise independent if

p(X₂|X₁) =p(X₂) (2.125)

and hence

p(X₂, X₁) =p(X₁)p(X₂|X₁) =p(X₁)p(X₂) (2.126)

We say thatnrandom variables are mutually independent if

p(X_i|X_S) =p(X_i) ∀S⊆ {1, . . . , n} \ {i} (2.127) and hence

p(X_1:n) = n i=1

p(X_i) (2.128)

Show that pairwise independence between all pairs of variables does not necessarily imply mutual inde-pendence. It suffices to give a counter example.

Exercise 2.8Conditional independence iff joint factorizes In the text we saidX ⊥Y|Z iff

p(x, y|z) =p(x|z)p(y|z) (2.129)

for allx, y, zsuch thatp(z)>0. Now prove the following alternative definition:X ⊥Y|Z iff there exist functiongandhsuch that

p(x, y|z) =g(x, z)h(y, z) (2.130)

for allx, y, zsuch thatp(z)>0.

Exercise 2.9Conditional independence

(Source: Koller.) Are the following properties true? Prove or disprove. Note that we are not restricting attention to distributions that can be represented by a graphical model.

a. True or false? (X ⊥W|Z, Y)∧(X⊥Y|Z)⇒(X⊥Y, W|Z) b. True or false?(X⊥Y|Z)∧(X⊥Y|W)⇒(X⊥Y|Z, W) Exercise 2.10Deriving the inverse gamma density

LetX∼Ga(a, b), i.e. Hint: use the change of variables formula.

Exercise 2.11Normalization constant for a 1D Gaussian

The normalization constant for a zero-mean Gaussian is given by Z=

Evaluate this integral and hence show Z = σ

(2π). Hint 1: separate the integral into a product of two terms, the first of which (involving dθ) is constant, so is easy. Hint 2: if u = e^−r2/2σ2 then du/dr=−_σ¹2re^−r2/2σ2, so the second integral is also easy (since

u(r)dr=u(r)).

Exercise 2.12Expressing mutual information in terms of entropies Show that

I(X, Y) =H(X)−H(X|Y) =H(Y)−H(Y|X) (2.136) Exercise 2.13Mutual information for correlated normals

(Source: (Cover and Thomas 1991, Q9.3).) Find the mutual informationI(X₁, X₂)whereXhas a bivariate normal distribution:

X₁ X₂

∼ N

0,

σ² ρσ² ρσ² σ²

(2.137) EvaluateI(X₁, X₂) forρ= 1,ρ= 0andρ=−1and comment. Hint: The (differential) entropy of a d-dimensional Gaussian is

h(X) = 1

2log₂ (2πe)^ddet Σ

(2.138) In the 1d case, this becomes

h(X) = 1 2log₂

2πeσ²

(2.139) Hint: log(0) =∞.

Exercise 2.14A measure of correlation (normalized mutual information)

(Source: (Cover and Thomas 1991, Q2.20).) LetXandY be discrete random variables which are identically distributed (soH(X) =H(Y)) but not necessarily independent. Define

r= 1−H(Y|X)

H(X) (2.140)

a. Showr=^I(X,Y_H(X)⁾ b. Show0≤r≤1 c. When isr= 0?

d. When isr= 1?

Exercise 2.15MLE minimizes KL divergence to the empirical distribution

Letp_emp(x)be the empirical distribution, and letq(x|θ)be some model. Show thatargmin_qKL(p_emp||q) is obtained byq(x) =q(x; ˆθ), whereθˆis the MLE. Hint: use non-negativity of the KL divergence.

Exercise 2.16Mean, mode, variance for the beta distribution Supposeθ∼Beta(a, b). Derive the mean, mode and variance.

Exercise 2.17Expected value of the minimum

SupposeX, Y are two points sampled independently and uniformly at random from the interval [0,1].

What is the expected location of the left most point?

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