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Conditional entropy H(X|Y) measures the average remaining uncertainty or surprise about a random variable X after another random variable Y has been observed. It is defined as the expected value of the conditional self-information h(x|y) over the joint distribution of X and Y.

Conditions: X and Y are discrete random variables.; The sum is taken over the joint alphabet $A_x \times A_y$.

Conditional entropy H(X|Y) measures the average remaining uncertainty or surprise about a random variable X after another random variable Y has been observed. It is defined as the expected value of the conditional self-information h(x|y) over the joint distribution of X and Y.

Conditions: X and Y are discrete random variables.; The sum is taken over the joint alphabet $A_x \times A_y$.

The exercise asks students to implement a Matlab function conditionalentropy(p) that computes the conditional entropy of X given Y from a 2D joint probability matrix $p(x,y)$. It includes specific test cases to verify correctness, such as deterministic and uniform distributions.

Conditions: Input p is assumed to be a 2D joint probability array $p(x,y)$.; Students may reuse existing entropy() and jointentropy() routines.

The exercise asks students to implement a Matlab function conditionalentropy(p) that computes the conditional entropy of X given Y from a 2D joint probability matrix $p(x,y)$. It includes specific test cases to verify correctness, such as deterministic and uniform distributions.

Conditions: Input p is assumed to be a 2D joint probability array $p(x,y)$.; Students may reuse existing entropy() and jointentropy() routines.