A sequential Monte Carlo framework for adaptive Bayesian model discrimination designs using mutual information

Drovandi, Christopher C., McGree, James, & Pettitt, Anthony N. (2014) A sequential Monte Carlo framework for adaptive Bayesian model discrimination designs using mutual information. In Lanzarone, Ettore & Ieva, Francesca (Eds.) Springer Proceedings in Mathematics & Statistics : the Contribution of Young Researchers to Bayesian Statistics, Springer, Milan, Italy, pp. 19-22.

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In this paper we present a unified sequential Monte Carlo (SMC) framework for performing sequential experimental design for discriminating between a set of models. The model discrimination utility that we advocate is fully Bayesian and based upon the mutual information. SMC provides a convenient way to estimate the mutual information. Our experience suggests that the approach works well on either a set of discrete or continuous models and outperforms other model discrimination approaches.

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ID Code: 69034
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
DOI: 10.1007/978-3-319-02084-6_5
ISBN: 9783319020846
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Springer
Deposited On: 24 Mar 2014 00:44
Last Modified: 07 Apr 2015 02:07

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