Riemann surface
matlab

Exercise 1
(a) Prove that the information measure is additive: that the information gained from observing the combination of $N$ independent events, whose probabilities are $p_i$ for $i=1 \ldots . . N$, is the sum of the information gained from observing each one of these events separately and in any order.

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Solution:
(b) By definition, $H=-\sum_i p_i \log _2 p_i$ is the entropy in bits for a discrete random variable distributed over states whose probabilities are $p_i$. So:
(i) In this case each $p_i=1 / 256$ and the ensemble entropy summation extends over 256 such equiprobable grey values, so $H=-(256)(1 / 256)(-8)=8$ bits.
(ii) Since all humans are in this category (humans $\subset$ mammals), there is no uncertainty about this classification and hence the entropy is 0 bits.
(iii) The entropy of this distribution is $-(1 / 4)(-2)-(1 / 4)(-2)-(1 / 2)(-1)=1.5$ bits.
(iv) By the definition of median, both classes have probability 0.5 , so the entropy is 1 bit.

(c) Consider two independent integer-valued random variables, $X$ and $Y$. Variable $X$ takes on only the values of the eight integers ${1,2, \ldots, 8}$ and does so with uniform probability. Variable $Y$ may take the value of any positive integer $k$, with probabilities $P{Y=k}=2^{-k}, k=1,2,3, \ldots$
(i) Which random variable has greater uncertainty? Calculate both entropies $H(X)$ and $H(Y)$.
(ii) What is the joint entropy $H(X, Y)$ of these random variables, and what is their mutual information $I(X ; Y)$ ?

(a) Suppose that women who live beyond the age of 80 outnumber men in the same age group by three to one. How much information, in bits, is gained by learning that a person who lives beyond 80 is male?

Solution:
(a) Rewriting “live beyond the age of 80 ” simply as “old”, we have the conditional probabilities $p($ female $\mid$ old $)=3 p($ male $\mid$ old $)$ and also of course $p($ female $\mid$ old $)+p($ male $\mid$ old $)=1$. It follows that $p($ male|old $)=1 / 4$. The amount of information (in bits) gained from an observation is $-\log _2$ of its probability. Thus the information gained by such an observation is 2 bits worth.
(b) Consider $n$ discrete random variables, named $X_1, X_2, \ldots, X_n$, of which $X_i$ has entropy $H\left(X_i\right)$, the largest being $H\left(X_L\right)$. What is the upper bound on the joint entropy $H\left(X_1, X_2, \ldots, X_n\right)$ of all these random variables, and under what condition will this upper bound be reached? What is the lower bound on the joint entropy $H\left(X_1, X_2, \ldots, X_n\right)$ ?

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