Transcription of Chapter 12 Bayesian Inference
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Chapter 12 Bayesian Inference
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.
Statistical Machine Learning CHAPTER 12. BAYESIAN INFERENCE where b = S n/n is the maximum likelihood estimate, e =1/2 is the prior mean and n = n/(n+2)⇡ 1. A 95 percent posterior interval can be obtained by numerically finding a and b such that
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