A Sequential Monte Carlo Algorithm to Incorporate Model Uncertainty in Bayesian Sequential Design

Abstract

This article presents a sequential Monte Carlo (SMC) algorithm that can be used for any one-at-a-time Bayesian sequential design problem in the presence of model uncertainty where discrete data are encountered. Our focus is on adaptive design for model discrimination but the methodology is applicable if one has a different design objective such as parameter estimation or prediction. An SMC algorithm is run in parallel for each model and the algorithm relies on a convenient estimator of the evidence of each model that is essentially a function of importance sampling weights. Methods that rely on quadrature for this task suffer from the curse of dimensionality. Approximating posterior model probabilities in this way allows us to use model discrimination utility functions derived from information theory that were previously difficult to compute except for conjugate models. A major benefit of the algorithm is that it requires very little problem-specific tuning. We demonstrate the methodology on three applications, including discriminating between models for decline in motor neuron numbers in patients suffering from motor neuron disease. Computer code to run one of the examples is provided as online supplementary materials.

Key Words:

• Entropy
• Model discrimination
• Mutual information
• Optimal design
• Particle filter

ACKNOWLEDGMENTS

This work was supported by the Australian Research Council. Two referees offered helpful comments to improve the article. The authors are grateful to Dr. Dave Woods for comments on an earlier draft. The authors also thank Elizabeth G. Ryan for proofreading the article.