Entropy and Information Theory

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Explores entropy bounds, conditional entropy theorems, and the chain rule for entropies, illustrating their application through examples.

Explains how entropy measures uncertainty in a system based on possible outcomes.

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

Explores mutual information, quantifying relationships between random variables and measuring information gain and statistical dependence.

Explores the Shannon-Fano algorithm for data compression and its efficiency in creating unique binary codes for letters.

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

Explores the Source Coding Theorem, entropy, Huffman coding, and conditioning's impact on entropy reduction.

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

Explores variational formulation for measuring information content and divergence between probability distributions.

Explains prefix-free codes for efficient data compression and the significance of uniquely decodable codes.