Vo, Brenda N

Drovandi, Christopher C

Pettitt, Anthony N

2019-03

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.

Bayesian Analysis, 14(1), p. 211-234

1931-6690

1936-0975

https://hdl.handle.net/1959.11/29588

International Society for Bayesian Analysis

Attribution 4.0 International

Bayesian Parametric Bootstrap for Models with Intractable Likelihoods

Journal Article

Publication