Advanced Bayesian Methods by Dr Gabriel Katz

This resource looks at modern Bayesian computation. Focusing on the two most widely used Bayesian algorithms, the Gibbs Sampler, and the Metropolis-Hastings. It reviews s criteria used to assess model convergence, and when running Bayesian models, how to identify that the model is ready to be used to draw inferences about parameters. It discusses the goodness of fit criteria used in the Bayesian world, which differs from those used in frequency statistics. It concludes by discussing methods to speed up conversion or speed up execution time. All videos in this resource uses the a single slide set, which can be accessed here.