Abstract: In Bayesian inference, a Bayes factor is defined as the ratio of posterior odds versus prior odds where posterior odds is simply a ratio of the normalizing constants of two posterior densities. In many practical problems, the two posteriors hav e different dimensions. For such cases, the current Monte Carlo methods such as the bridge sampling method (Meng and Wong (1996)), the path sampling method (Gelman and Meng (1994)), and the ratio importance sampling method (Chen and Shao (1997)) cannot directly be applied. In this article, we extend importance sampling, bridge sampling, and ratio importance sampling to problems of different dimensions. Then we find global optimal importance sampli ng, bridge sampling, and ratio importance sampling in the sense of minimizing asymptotic relative mean-square errors of estimators. Implementation algorithms, which can asymptotically achieve the optimal simulation errors, are developed and two illustrative examples are also provided.
Key words and phrases: Bayesian computation, Bayes factor, bridge sampling, Gibbs sampler, importance sampling, Markov chain Monte Carlo, Metropolis algorithm, ratio importance sampling.