Distributed Bayesian Computation for Model Choice

We derive a general decomposition of the model evidence that allows an efficient divide-and-conquer calculation on every worker without accessing the data in one single place. The combination of the results requires only minimal communication between the workers and no exchange of data. We illustrate the applicability of our method on several challenging applications and show that the computation time is reduced by several orders of magnitude, incurring only a negligible bias. We show how to apply our approach in a reversible jump setting where an MCMC sampler moves between different models. The rest of our work is structured as follows: we discuss related work in Section 2 before presenting our approach on distributed Bayesian model choice in Section 3. In Section 4 we demonstrate the applicability of our approach on several data sets and models before discussing possible extensions in Section 5.