Bayesian parametric bootstrap for models with intractable likelihoods

Vo, Brenda N., Drovandi, Christopher C., & Pettitt, Anthony N. (2015) Bayesian parametric bootstrap for models with intractable likelihoods. [Working Paper] (Unpublished)


In this paper it is demonstrated how the Bayesian parametric bootstrap can be adapted to models with intractable likelihoods. The approach is most appealing when the semi-automatic approximate Bayesian computation (ABC) summary statistics are selected. After a pilot run of ABC, the likelihood-free parametric bootstrap approach requires very few model simulations to produce an approximate posterior, which can be a useful approximation in its own right. An alternative is to use this approximation as a proposal distribution in ABC algorithms to make them more efficient. In this paper, the parametric bootstrap approximation is used to form the initial importance distribution for the sequential Monte Carlo and the ABC importance and rejection sampling algorithms. The new approach is illustrated through a simulation study of the univariate g-and- k quantile distribution, and is used to infer parameter values of a stochastic model describing expanding melanoma cell colonies.

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11 citations in Web of Science®
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110 since deposited on 31 Aug 2015
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ID Code: 86986
Item Type: Working Paper
Refereed: No
Keywords: Bayesian bootstrap, Approximate Bayesian computation, sequential Monte Carlo, melanoma cell spreading, importance sampling, quantile distribution
Subjects: Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Biostatistics (010402)
Australian and New Zealand Standard Research Classification > MATHEMATICAL SCIENCES (010000) > STATISTICS (010400) > Stochastic Analysis and Modelling (010406)
Australian and New Zealand Standard Research Classification > BIOLOGICAL SCIENCES (060000) > BIOCHEMISTRY AND CELL BIOLOGY (060100) > Synthetic Biology (060113)
Divisions: Current > Research Centres > ARC Centre of Excellence for Mathematical & Statistical Frontiers (ACEMS)
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2015 QUT & the Authors
Deposited On: 31 Aug 2015 22:37
Last Modified: 01 Sep 2015 23:31

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