When conditional entropy H(X|Y) is zero, it means there is no remaining uncertainty or surprise about X once Y is known. This implies that X is a deterministic function of Y, so all information about X is completely contained within Y.
Conditions: X and Y are discrete random variables.
When conditional entropy H(X|Y) is zero, it means there is no remaining uncertainty or surprise about X once Y is known. This implies that X is a deterministic function of Y, so all information about X is completely contained within Y.
Conditions: X and Y are discrete random variables.
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.