Physical Sciences and Mathematics Commons^™

Computing The Bayes Factor From A Markov Chain Monte Carlo Simulation Of The Posterior Distribution, Martin D. Weinberg

Astronomy Department Faculty Publication Series

Computation of the marginal likelihood from a simulated posterior distribution is central to Bayesian model selection but is computationally difficult. I argue that the marginal likelihood can be reliably computed from a posterior sample by careful attention to the numerics of the probability integral. Posing the expression for the marginal likelihood as a Lebesgue integral, we may convert the harmonic mean approximation from a sample statistic to a quadrature rule. As a quadrature, the harmonic mean approximation suffers from enormous truncation error as consequence . In addition, I demonstrate that the integral expression for the harmonic-mean approximation converges slowly at …