Expected Number of Visits in State

Related lectures (29)

Page 1 of 3

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

Explores probability convergence, discussing conditions for random variable sequences to converge and the uniqueness of convergence.

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

Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.

Covers applied probability, stochastic processes, Markov chains, rejection sampling, and Bayesian inference methods.

Explores the strong convergence of random variables and the normal distribution approximation in probability and statistics.

Covers moments, variance, and expected values in probability and statistics, including the distribution of tokens in a product.

Covers the theory and applications of Markov chains in modeling random phenomena and decision-making under uncertainty.

Covers stochastic models for communications, focusing on random variables, Markov chains, Poisson processes, and probability calculations.

Explores a first-order Markov model using a sunny-rainy source example, demonstrating how past events influence future outcomes.