Markov Chain Monte Carlo Methods for Computing Bayes Factors
计算 Bayes 因子的马尔可夫链蒙特卡洛方法
The problem of calculating posterior probabilities for a collection of competing models and associated Bayes factors continues to be a formidable challenge for applied Bayesian statisticians. Current approaches that take advantage of modern Markov chain Monte Carlo computing methods include those that attempt to sample over some form of the joint space created by the model indicators and the parameters for each model, others that sample over the model space alone, and still…