Markov Chains: Properties and Expectations

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

Explores fundamental theorems in probability and statistics, joint probability laws, and marginal distributions.

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