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This online calculator calculates entropy of Y random variable conditioned on X random variable and X random variable conditioned on Y random variable given a joint distribution table (X, Y) ~ p. The conditional entropy H (Y|X) is the amount of information needed to describe the outcome of a random variable Y given that the value of another ...
In information theory, the conditional entropy quantifies the amount of information needed to describe the outcome of a random variable given that the value of another random variable is known. Here, information is measured in shannons, nats, or hartleys.
13 Μαΐ 2020 · Conditional Entropy. How does entropy change when we know something about the outcome? Lets suppose we know a day is cloudy \(49\%\) of the time, and the remaining \(51\%\) of the time it is not cloudy. The entropy of such a distribution is \(\simeq1\). Now imagine we are told if it is raining or not, with the following probabilities:
2.1 Example. Suppose you have a random variable X such that: = X 0 with prob p. 1 with prob 1 − p, then the entropy of X is given by. H(X) = −p log p − (1 − p) log(1 − p) = H(p) (2) (3) Note that the entropy does not depend on the values that the random variable takes (0 and 1 in this case), but only depends on the probability distribution p(x). 1.
2 Σεπ 2019 · Formula for conditional entropy is: $H(X|Y)=\sum_{v\epsilon values(Y)}P(Y=v)H(X|Y=v)$ for X given Y. Mutual information of X and Y: $I(X,Y)=H(X)-H(X|Y)=H(Y)-H(Y|X)$ I assume you already know the formula for H(X), the entropy.
This online calculator calculates entropy of Y random variable conditioned on X random variable and X random variable conditioned on Y random variable given a joint distribution table (X, Y) ~ p.
Theorem: Entropy. If X is a binary random variable with the distribution f (0) = p and. f (1) = 1 p, then: H(X) = 0 if p = 0 or p = 1 max H(X) for p = 1. 2. Intuitively, an entropy of 0 means that the outcome of the random variable is determinate; it contains no information (or uncertainty).
