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Bayesian Inference: From Priors to Posterior Approximation

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Bayesian inference takes you from a prior and a likelihood to a posterior you can summarize, test with a posterior predictive check, and compare. You learn when a conjugate prior is exact, how hierarchical Bayesian models share strength, and why most real posteriors need Markov chain Monte Carlo.

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What You'll Learn

Concepts:
Variational Inference Bayes Factor Markov Chain Monte Carlo Weakly Informative Prior Posterior Predictive Distribution Credible Interval Partial Pooling Normal-Normal Conjugate Model Beta-Binomial Model

What you will learn

No introduction video available

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