Estimating the evidence – a review
作者： Nial Friel , Jason Wyse
DOI: 10.1111/J.1467-9574.2011.00515.X
关键词:
摘要: The model evidence is a vital quantity in the comparison of statistical models under Bayesian paradigm. This study presents review commonly used methods. We outline some guidelines and offer practical advice. reviewed methods are compared for two examples; non-nested Gaussian linear regression covariate subset selection logistic regression.
harvard.edu
arxiv-vanity.com参考文章(22)
Radford M. Neal, Invited comment on the paper "Slice Sampling" by Radford Neal Ims Institute of Mathematical Statistics. ,(2003) , 10.1214/AOS/1056562461
Radford M. Neal, Annealed importance sampling Statistics and Computing. ,vol. 11, pp. 125- 139 ,(2001) , 10.1023/A:1008923215028
Chris T. Volinsky, Adrian E. Raftery, David Madigan, Jennifer A. Hoeting, Bayesian model averaging: a tutorial (with comments by M. Clyde, David Draper and E. I. George, and a rejoinder by the authors Statistical Science. ,vol. 14, pp. 382- 417 ,(1999) , 10.1214/SS/1009212519
Michael A. Newton, Adrian E. Raftery, Approximate Bayesian Inference with the Weighted Likelihood Bootstrap Journal of the Royal Statistical Society: Series B (Methodological). ,vol. 56, pp. 3- 26 ,(1994) , 10.1111/J.2517-6161.1994.TB01956.X
John Skilling, Nested sampling for general Bayesian computation Bayesian Analysis. ,vol. 1, pp. 833- 859 ,(2006) , 10.1214/06-BA127
Siddhartha Chib, Ivan Jeliazkov, Marginal Likelihood From the Metropolis–Hastings Output Journal of the American Statistical Association. ,vol. 96, pp. 270- 281 ,(2001) , 10.1198/016214501750332848
Andrew Gelman, Xiao-Li Meng, Simulating normalizing constants: from importance sampling to bridge sampling to path sampling Statistical Science. ,vol. 13, pp. 163- 185 ,(1998) , 10.1214/SS/1028905934
Siddhartha Chib, Marginal Likelihood from the Gibbs Output Journal of the American Statistical Association. ,vol. 90, pp. 1313- 1321 ,(1995) , 10.1080/01621459.1995.10476635
Luke Tierney, Joseph B. Kadane, Accurate Approximations for Posterior Moments and Marginal Densities Journal of the American Statistical Association. ,vol. 81, pp. 82- 86 ,(1986) , 10.1080/01621459.1986.10478240
Stuart Geman, Donald Geman, Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images IEEE Transactions on Pattern Analysis and Machine Intelligence. ,vol. PAMI-6, pp. 721- 741 ,(1984) , 10.1109/TPAMI.1984.4767596