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About Bayesian Inference: From Priors to Posterior Approximation

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You will be able to build a Bayesian model end to end: encode belief as a prior, combine it with a likelihood into a posterior, and use the evidence for model comparison with Bayes factors. You will work conjugate models (beta-binomial, normal-normal) by hand, choose weakly informative priors deliberately, summarize uncertainty with credible intervals and posterior predictive checks, and pool grouped data with hierarchical models. Finally you will understand why the normalizing constant makes most real posteriors intractable, and how MCMC and variational inference answer that problem from two different directions.

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What You'll Learn

Concepts:
Variational Inference Hierarchical Bayesian Model Intractable Normalizing Constant Bayes Factor Markov Chain Monte Carlo Weakly Informative Prior Posterior Predictive Distribution Credible Interval Marginal Likelihood Partial Pooling Posterior Distribution Normal-Normal Conjugate Model Beta-Binomial Model Prior Distribution Conjugate Prior Likelihood Function

What you will learn

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