Likelihood-free extensions for Bayesian sequentially designed experiments

Hainy, Markus, Drovandi, Christopher C., & McGree, James (2016) Likelihood-free extensions for Bayesian sequentially designed experiments. In Kunert, Joachim, Muller, Christine H., & Atkinson, Anthony C. (Eds.) Proceedings of the 11th International Workshop in Model-Oriented Design, Springer, Hamminkeln, Germany, pp. 153-161.

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When considering a Bayesian sequential design framework, sequential Monte Carlo (SMC) algorithms are a natural approach. However, these algorithms require the likelihood function to be evaluated. Therefore, they cannot be applied in cases where the likelihood is not available or is intractable. To overcome this limitation, we propose likelihood-free extensions of the standard SMC algorithm. We also investigate a specific simulation-based approximation of the likelihood known as the synthetic likelihood. The algorithms are applied and tested on a well-studied sequential design problem for estimating a non-linear function of linear regression parameters.

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ID Code: 98453
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
DOI: 10.1007/978-3-319-31266-8_18
ISBN: 9783319312668
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Applied Statistics (010401)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Statistical Theory (010405)
Divisions: Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Springer International Publishing Switzerland 2016
Deposited On: 29 Aug 2016 02:07
Last Modified: 03 Sep 2016 10:40

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