Bayesian Multimodel Inference by RJMCMC: A Gibbs Sampling Approach
暂无分享,去创建一个
[1] William A. Link,et al. Bayes Factors and Multimodel Inference , 2009 .
[2] Xiao-Li Meng,et al. SIMULATING RATIOS OF NORMALIZING CONSTANTS VIA A SIMPLE IDENTITY: A THEORETICAL EXPLORATION , 1996 .
[3] G. Casella,et al. Rao-Blackwellisation of sampling schemes , 1996 .
[4] M. Newton. Approximate Bayesian-inference With the Weighted Likelihood Bootstrap , 1994 .
[5] P. Green. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .
[6] S. Chib. Marginal Likelihood from the Gibbs Output , 1995 .
[7] William A Link,et al. Model weights and the foundations of multimodel inference. , 2006, Ecology.
[8] R. O’Hara,et al. A review of Bayesian variable selection methods: what, how and which , 2009 .
[9] George A. F. Seber,et al. A matrix handbook for statisticians , 2007 .
[10] S. Godsill. On the Relationship Between Markov chain Monte Carlo Methods for Model Uncertainty , 2001 .
[11] Peter Congdon,et al. Bayesian model choice based on Monte Carlo estimates of posterior model probabilities , 2006, Comput. Stat. Data Anal..
[12] B. Carlin,et al. Bayesian Model Choice Via Markov Chain Monte Carlo Methods , 1995 .
[13] Jean-Michel Marin,et al. On some difficulties with a posterior probability approximation technique , 2008 .
[14] L. Wasserman,et al. Computing Bayes Factors by Combining Simulation and Asymptotic Approximations , 1997 .
[15] A. Mira,et al. Efficient Bayes factor estimation from the reversible jump output , 2006 .
[16] Bradley P. Carlin,et al. Bayesian measures of model complexity and fit , 2002 .