Trans-dimensional Metropolis-Hastings Using Parallel Chains

Sally A. Wood, Melbourne Business School, University of Melbourne
James Pullen, University of New South Wales
Robert Kohn, University of New South Wales
David Leslie, University of Bristol

Abstract

A general Bayesian sampling method is developed that uses parallel chains to select between models and to average the predictive density over such models. The method applies to both non-nested models and to nested models, and is particularly useful for mixtures of complex component models, where a novel approach to overcome the label-switching problem is used. The method is illustrated with real and simulated data in model-averaging over alternative financial time series models, mixtures of normal distributions, and mixtures of smoothing spline models.