Perbandingan modél Bayes

modél data posterior probabiliti, P(H|D), ngagunakeun Bayes' theorem:
P(H|D) = P(D|H)P(H)/P(D)

Wates konci data-dependent P(D|H) nyaéta likelihood, jeung kadangkala disebut kajadian keur modél H; evaluasi nu bener mangrupa konci dina modél perbandingan Bayes.

Kajadian umumna normalizing constant atawa partition function tina kaputusan séjén, disebut modél paramater kaputusan H ti data D.

Hal nu asup akal di modél dua béda H1 jeung H2, parametrised ku modél vektor  jeung  nu ditaksir maké Bayes factor dirumuskeun ku

Sumber sejen 
 Gelman, A., Carlin, J.,Stern, H. and Rubin, D. Bayesian Data Analysis. Chapman and Hall/CRC.(1995)
 Bernardo, J., and Smith, A.F.M., Bayesian Théory. John Wiley. (1994)
 Lee, P.M. Bayesian Statistics. Arnold.(1989).
 Denison, D.G.T., Holmes, C.C., Mallick, B.K., Smith, A.F.M., Bayesian Methods for Nonlinéar Classification and Regression. John Wiley. (2002).
 Richard O. Duda, Peter E. Hart, David G. Stork (2000) Pattern classification (2nd edition), Section 9.6.5, p. 487-489, Wiley, ISBN 0471056693
 Chapter 24 in Probability Theory - The logic of science by E. T. Jaynes, 1994.
 David J.C. MacKay (2003) Information théory, inference and léarning algorithms, CUP, ISBN 0521642981, (also available online)

Tumbu kaluar 

 The on-line textbook: Information Theory, Inference, and Learning Algorithms, by David J.C. MacKay, has many chapters on Bayesian methods, including introductory examples; compelling arguments in favour of Bayesian methods; state-of-the-art Monte Carlo methods, message-passing methods, and variational methods; and examples illustrating the intimate connections between Bayesian inference and data compression.