Optimization and Simulation: Bayesian Inference

This lecture covers the application of Markov Chain Monte Carlo methods, including Metropolis-Hastings and Gibbs sampling, in optimization and simulation. It explores the knapsack problem, Bayesian inference, and prediction using Bayesian and frequentist approaches. The instructor discusses the challenges of drawing from complex distributions and the critical role of simulation in Bayesian inference.