Probability Theory: Joint Marginals and Granger Causality

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Explores tail bounds, information bounds, and maximal leakage in the context of generalization error.

Explores examples of guessing letters, origins of entropy, and properties in information theory.

Explores conditional probability, Bayes' theorem, and prediction decomposition for informed decision-making.

Covers the basics of probability theory, random variables, joint probability, and inference.

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

Explores joint random variables, conditional density, and independence in probability and statistics.

Discusses conditional entropy and its role in data compression techniques.

Explores entropy in information theory and its role in data processing and probability distributions.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

Explores mutual information and entropy calculation between random variables.