Bayesian Analysis

Bayesian Parametric Bootstrap for Models with Intractable Likelihoods

Brenda N. Vo, Christopher C. Drovandi, and Anthony N. Pettitt

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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 computationally efficient semi-automatic approximate Bayesian computation (ABC) summary statistics are selected. The parametric bootstrap approximation is used to form a proposal distribution in ABC algorithms to improve the computational efficiency. The new approach is demonstrated through the sequential Monte Carlo and the ABC importance and rejection sampling algorithms. We found efficiency gains in two simulation studies, the univariate g-and-k quantile distribution, a toggle switch model in dynamic bionetworks, and in a stochastic model describing expanding melanoma cell colonies.

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Bayesian Anal., Volume 14, Number 1 (2019), 211-234.

First available in Project Euclid: 26 April 2018

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Bayesian parametric bootstrap approximate Bayesian computation sequential Monte Carlo melanoma cell spreading agent-based model quantile distribution

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Vo, Brenda N.; Drovandi, Christopher C.; Pettitt, Anthony N. Bayesian Parametric Bootstrap for Models with Intractable Likelihoods. Bayesian Anal. 14 (2019), no. 1, 211--234. doi:10.1214/17-BA1071.

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