Generalization of Martingales: Sub- & Supermartingales

Related lectures (31)

Page 3 of 4

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

Covers characteristic functions for random variables and their properties, including independence and convergence.

Covers the basics of Martingale theory and its applications in random variables.

Explores uniform integrability, convergence theorems, and the importance of bounded sequences in understanding the convergence of random variables.

Explores covariance, correlation, and mutual information in quantifying statistical dependence between random variables.

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

Explores the convergence of random variables, the law of large numbers, and the distribution of failure time.

Covers random variables, sample spaces, probability distributions, functions, expected value, variance, and estimations.

Explores continuous random variables, density functions, joint variables, independence, and conditional densities.

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