Martingale Convergence Theorem

Related lectures (32)

Page 3 of 4

Explores the generalization of martingales to sub- and supermartingales with a focus on convergence properties.

Covers the proof and recap of the martingale convergence theorem, focusing on the conditions for the existence of a random variable.

Covers random vectors, joint probability density, independent random variables, functions of two random variables, and Gaussian random variables.

Introduces subgaussian and subexponential random variables, conditional expectation, and Orlicz norms.

Covers probability, random variables, expectation, GLMs, hypothesis testing, and Bayesian statistics with practical examples.

Explores the properties and definition of conditional expectation in random variables.

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

Introduces random variables, probability distributions, and expected values through practical examples.

Explores conditional expectation properties, including measurability, linearity, and independence of random variables.

Covers the Central Limit Theorem and its application to random variables, proving convergence to a normal distribution.