Trans-dimensional Metropolis-Hastings using parallel chains
by Sally Wood
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 ï¬nancial time series models, mixtures of normal distributions, and mixtures of smoothing spline models.
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