Mutual Information and Entropy

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Explores random walks, Moran model, bacterial chemotaxis, entropy, information theory, and coevolving sites in proteins.

Explores covariance, correlation, and mutual information in quantifying statistical dependence between random variables.

Explores channel capacity and convex functions in information theory, emphasizing the importance of convexity.

Explores entropy, uncertainty, coding theory, and data compression applications.

Covers the basics of information theory, focusing on Shannon's setting and channel transmission.

Discusses conditional entropy and its role in data compression techniques.

Introduces random variables and their significance in information theory, covering concepts like expected value and Shannon's entropy.

On entropy and mutual information explores quantifying information in data science through probability distributions.

Covers source coding theorems, entropy, and various source models in information theory.

Introduces information theory basics, including entropy, independence, and binary entropy function.