Markov Chains: Ergodicity and Stationary Distribution

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

Covers stochastic models for communications, focusing on discrete-time Markov chains.

Explores reversibility in Markov chains and its impact on the stationary distribution, highlighting the complexity of non-reversible chains.

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