high performance computing on graphics processing units: hgpu.org

The sequential Monte Carlo (smc) methods have been widely used for modern scientific computation. Bayesian model comparison has been successfully applied in many fields. Yet there have been few researches on the use of smc for the purpose of Bayesian model comparison. This thesis studies different smc strategies for Bayesian model computation. In addition, various extensions and refinements of existing smc practices are proposed in this thesis. Through empirical examples, it will be shown that the smc strategies can be applied for many realistic applications which might be difficult for Markov chain Monte Carlo (mcmc) algorithms. The extensions and refinements lead to an automatic and adaptive strategy. This strategy is able to produce accurate estimates of the Bayes factor with minimal manual tuning of algorithms. Another advantage of smc algorithms over mcmc algorithms is that it can be parallelized in a straightforward way. This allows the algorithms to better utilize modern computer resources. This thesis presents work on the parallel implementation of generic smc algorithms. A C++ framework within which generic smc algorithms can be implemented easily on parallel computers is introduced. We show that with little additional effort, the implementations using this framework can provide significant performance speedup.