Probability & Stochastic Processes

This lecture covers topics on applied probability and stochastic processes. It starts with a midterm project involving coin tossing scenarios and Markov chain models of undirected weighted graphs. The lecture then delves into Markov Chain Monte Carlo (MCMC) methods, focusing on rejection sampling and Bayesian inference. It explains the process of generating sample values from a target distribution using proposal distributions and estimating unknown parameters. The lecture concludes with discussions on defining transition probabilities, updating rules, and invariant distributions in MCMC algorithms.