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Chapter 12 Bayesian Inference
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Chapter 12 Bayesian Inference - Carnegie Mellon University
www.stat.cmu.eduStatistical 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
Pattern Recognition and Machine Learning by Bishop
tommyodland.comBayesian inference Gaussian variables. { To estimate N (˙2 is assumed known), use Gaussian prior. { To estimate = 1=˙2, use Gamma function as prior, i.e. Gam( ja;b) = ba a 1 ( a) exp( b ) since it has the same functional form as the likelihood. The Student-t distribution may be motivated by: { Adding an in nite number of Gaussians with ...