Martingale Theory: Basics and Applications

Related lectures (32)

Page 3 of 4

Explores conditional expectation, the grouping lemma, and the law of large numbers.

Covers the von Neumann Extractor, its extensions, extraction rules, uniformity proofs, and exchangeability of sequences.

Covers independence between random variables and product measures in probability theory.

Covers the calculation of expectation and variance for different types of random variables, including discrete and continuous ones.

Explores the transformation of random variables, joint moments, covariance, and variance.

Covers the properties of expectation and examples of calculating expectations for random variables.

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

Covers the pdf of the sum of continuous random variables, convolution, and transformations like Y=aX.

Covers random variables, probability functions, expectations, variances, and joint distributions.

Discusses integrability, square-integrability, boundedness, centering, and linearity of random variables.