Bayesian Model Discrimination and Bayes Factors for Linear Gaussian State Space Models

Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 1995 · Volume 57 · Issue 1

DOI: 10.1111/j.2517-6161.1995.tb02027.x

Abstract

It is shown how to discriminate between different linear Gaussian state space models for a given time series by means of a Bayesian approach which chooses the model that minimizes the expected loss. A practical implementation of this procedure requires a fully Bayesian analysis for both the state vector and the unknown hyperparameters and is carried out by Markov chain Monte Carlo methods. An application to some non-standard situations such as testing hypotheses on the boundary of the parameter space, discriminating non-nested models and discrimination of more than two models is discussed in detail.

Keywords

bayes factors, markov chain monte carlo method, model discrimination, model likelihood, state space models, training sample priors

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