Transcription of Chapter 12 Bayesian Inference - Carnegie Mellon University
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Chapter 12 Bayesian Inference - Carnegie Mellon University
Chapter 12 Bayesian InferenceThis Chapter covers the following topics: Concepts and methods of Bayesian Inference . Bayesian hypothesis testing and model comparison. Derivation of the Bayesian information criterion (BIC). Simulation methods and Markov chain monte Carlo (MCMC). Bayesian computation via variational Inference . Some subtle issues related to Bayesian What is Bayesian Inference ?There are two main approaches to statistical machine learning:frequentist(or classical)methods andBayesianmethods. Most of the methods we have discussed so far are fre-quentist. It is important to understand both approaches. At the risk of oversimplifying, thedifference is this:Frequentist versus Bayesian Methods In frequentist Inference , probabilities are interpreted as long run goal is to create procedures with long run frequency guarantees. In Bayesian Inference , probabilities are interpreted as subjective degrees of be-lief.
Chapter 12 Bayesian Inference This chapter covers the following topics: • Concepts and methods of Bayesian inference. • Bayesian hypothesis testing and model comparison. • Derivation of the Bayesian information criterion (BIC). • Simulation methods and Markov chain Monte Carlo (MCMC). • Bayesian computation via variational inference.
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