Perbandingan modél Bayes
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Model 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) nyaeta likelihood, jeung kadangkala disebut kajadian keur model H; evaluasi nu bener ngarupakeun konci dina model perbandingan Bayes.
Kajadian umumna normalizing constant atawa partition function tina kaputusan sejen, disebut model paramater kaputusan H ti data D.
Hal nu asup akal di model dua beda H1 jeung H2, parametrised ku model vektor θ1 jeung θ2 nu ditaksir make Bayes factor dirumuskeun ku
[édit] 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 Theory. 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 Nonlinear 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 theory, inference and learning algorithms, CUP, ISBN 0521642981, (also available online)
[édit] 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.</math>
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