Open Access. Powered by Scholars. Published by Universities.®
Physical Sciences and Mathematics Commons™
Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 1 of 1
Full-Text Articles in Physical Sciences and Mathematics
Computing The Bayes Factor From A Markov Chain Monte Carlo Simulation Of The Posterior Distribution, Martin D. Weinberg
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 …