Lecture
This lecture covers Bayesian estimation, starting with an overview of classical statistical inference versus Bayesian inference. It then delves into topics such as conjugate priors, Markov Chain Monte Carlo (MCMC) methods, and examples of Bayesian estimation for binary logit and logit mixture models. The instructor explains the importance of priors, likelihood, and posteriors in Bayesian inference, showcasing a temperature example to illustrate the concepts. The lecture also explores the use of Gibbs sampling and Metropolis-Hastings algorithms in MCMC, along with the assessment of convergence using Gelman and Rubin diagnostics. A case study on the choice of grapes demonstrates the practical application of Bayesian estimation in predicting individual behavior and market demand.