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Building on previous CSDE Workshop, Introduction to Bayesian Statistics: Likelihoods, Priors & Posteriors, in the 2nd part of our 3-part series on Bayesian Statistics– Introduction to Bayesian Statistics: Inference– covers the basics of how Bayesian inference is performed and how it compares to frequentist inference methods you already know. We cover inference in conjugate family settings using analytic posteriors and grid approximation in R.

Materials

https://github.com/jlgodwin/IntroBayesPartII