Difference between revisions of "Entropy, Relative Entropy, Mutual Information"

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{{NumBlk|:|<math>E\left[\log_2\left(\tfrac{1}{p\left(x\right)}\right)\right]=\sum_{x\in \mathcal{X}} \log_2\left(\tfrac{1}{p\left(x\right)}\right) \cdot p\left(x\right)=-\sum_{x\in \mathcal{X}} p\left(x\right) \cdot \log_2 p\left(x\right)=H\left(X\right)</math>|{{EquationRef|4}}}}
 
{{NumBlk|:|<math>E\left[\log_2\left(\tfrac{1}{p\left(x\right)}\right)\right]=\sum_{x\in \mathcal{X}} \log_2\left(\tfrac{1}{p\left(x\right)}\right) \cdot p\left(x\right)=-\sum_{x\in \mathcal{X}} p\left(x\right) \cdot \log_2 p\left(x\right)=H\left(X\right)</math>|{{EquationRef|4}}}}
  
==== Lemma ====
+
=== Lemma 1 ===
 
<math>H\left(X\right)\ge 0</math>
 
<math>H\left(X\right)\ge 0</math>

Revision as of 16:29, 25 June 2020

Definitions

Entropy

  • a measure of the uncertainty of a random variable
  • The entropy of a random variable is a measure of the uncertainty of the random variable
    • it is a measure of the amount of information required on the average to describe the random variable

Relative Entropy

  • a measure of the distance between two distributions
  • a measure of the inefficiency of assuming that the distribution is when the true distribution is .

Mutual Information

  • a measure of the amount of information that one random variable contains about another random variable

Entropy

Definitions:

  • a measure of the uncertainty of a random variable
  • The entropy of a random variable is a measure of the uncertainty of the random variable
    • it is a measure of the amount of information required on the average to describe the random variable

The entropy of a discrete random variable, , is

 

 

 

 

(1)

where has a probability mass function (pmf), , and an alphabet .

Expected Value

For a discrete random variable, , with probability mass function, , the expected value of is

 

 

 

 

(2)

For a discrete random variable, , with probability mass function, , the expected value of is

 

 

 

 

(3)

Consider the case where . We get

 

 

 

 

(4)

Lemma 1