Martingales: More Theory

Related lectures (30)

Page 3 of 3

Covers the Law of Large Numbers, Central Limit Theorem, Chernoff bounds, and PAC bounds in probability theory.

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

Introduces statistical analysis concepts like boxplot and normal distribution using real data examples.

Introduces statistics basics, including data analysis and probability theory, emphasizing central tendency, dispersion, and distribution shapes.

Explores Ramanujan graphs, generating functions, non-backtracking walks, and expander graphs in relation to NP-hard problems.

Explores Hertz theory for contact problems and the Tabor measurement method.

Covers the solutions to the midterm exam of a Probability Theory course, including calculations of probabilities and expectations.

Explores expanders, Ramanujan graphs, eigenvalues, Laplacian matrices, and spectral properties.

Covers basic properties of conditional expectation and Jensen's inequality in probability theory.

Explores the Central Limit Theorem, covariance, correlation, joint random variables, quantiles, and the law of large numbers.