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Please answer question 4 4 ) Answer why entropy is maximized in a uniform distribution. ( 2 5 points ) Self - Information In information

Please answer question 4
4) Answer why entropy is maximized in a uniform distribution. (25points)
Self-Information
In information theory, the entropy of a random variable is the average level of "information",
"surprise", or "uncertainty" inherent to the variable's possible outcomes.
The self-information is a measure of the information content associated with the outcome of a
random variable. The self-information of an event x=x is defined as:
I(x)=-log2P(x=x)
The choice of base for log, the logarithm, varies for different applications. Base 2 gives the unit of
bits. We can quantify the amount of uncertainty in an entire probability distribution using the
Shannon entropy.
Shannon Entropy
Given a discrete random variable x, with possible outcomes x1,dots,xn, which occur with probability
P(x=x1),dots,P(x=xn) the entropy of x is formally defined as:
H(x)=-i=1nP(x=xi)log2P(x=xi)
where ?? denotes the sum over the variable's possible values. An equivalent definition of entropy is
the expected value of self-information of a variable.
Problem:
Study Shannon Entropy yourself in more detail and calculate the entropy of two random
variables x and Y, respectively.
Random variable x is a uniform random variable with N=8, i.e.,xU(8).(25 points)
Random variable Y has the following probability mass function (25 points):
P(x=1)=12,P(x=2)=14,P(x=3)=18,P(x=4)=116,
P(x=5)=164,P(x=6)=164,P(x=7)=164,P(x=8)=164.
Which random variable gives a higher entropy, x or Y?(25 points)
Answer why entropy is maximized in a uniform distribution. (25 points)
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