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.
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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|Item Type:||Conference Paper|
|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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