Bayesian Analysis

Bayesian Parametric Bootstrap for Models with Intractable Likelihoods

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

Full-text: Open access

Abstract

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.

Article information

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

Dates
First available in Project Euclid: 26 April 2018

Permanent link to this document
https://projecteuclid.org/euclid.ba/1524729725

Digital Object Identifier
doi:10.1214/17-BA1071

Mathematical Reviews number (MathSciNet)
MR3910044

Zentralblatt MATH identifier
07001981

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

Rights
Creative Commons Attribution 4.0 International License.

Citation

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. https://projecteuclid.org/euclid.ba/1524729725


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