Entropy Bounds: Conditional Entropy Theorems

Related lectures (28)

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

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

Covers the application of entropy in algorithms, focusing on sorting and decision-making strategies.

Covers conditional entropy, weather conditions, function entropy, and the chain rule.

Explores convergence in law for random variables, including Kolmogorov's theorem and proofs based on probability lemmas.

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

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

Covers the Central Limit Theorem and its application to random variables, proving convergence to a normal distribution.

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

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