The following video motivates why computational probabilistic methods and probabilistic programming are important part of modern Bayesian data analysis.
Short video clips on selected introductory topics are available in a Panopto folder and listed below.
2019 fall lecture videos are in a Panopto folder and listed below.
Lecture 2.1 and Lecture 2.2 on basics of Bayesian inference, observation model, likelihood, posterior and binomial model, predictive distribution and benefit of integration, priors and prior information, and one parameter normal model (BDA3 Ch 1+2).
Lecture 3 on multiparameter models, joint, marginal and conditional distribution, normal model, bioassay example, grid sampling and grid evaluation (BDA3 Ch 3).
Lecture 4.1 on numerical issues, Monte Carlo, how many simulation draws are needed, how many digits to report, and Lecture 4.2 on direct simulation, curse of dimensionality, rejection sampling, and importance sampling (BDA3 Ch 10).
Lecture 12.1 on frequency evaluation, hypothesis testing and variable selection and Lecture 12.2 overview of modeling data collection (Ch8), linear models (Ch. 14-18), lasso, horseshoe and Gaussian processes (Ch 21).