Explores probability convergence, discussing conditions for random variable sequences to converge and the uniqueness of convergence.
Covers entropy, source coding, encoding maps, decodability, prefix-free codes, and Kraft-McMillan's inequality.
Covers the basics of information theory, focusing on Shannon's setting and channel transmission.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.
Covers conditional expectation, Fubini's theorem, and their applications in probability theory.
