Lectures related to Source Coding Theorems: Entropy and Source Models

Introduces key concepts in probability and random variables, covering statistics, distributions, and covariance.

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

Delves into probability, statistics, paradoxes, and random variables, showcasing their real-world applications and properties.

Covers the basics of statistical theory, including probability models, random variables, and sampling distributions.

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

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