BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood
Dublin Core
Title
BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood
Subject
approximate Bayesian computation, covariance matrix estimation, Markov chain
Monte Carlo, likelihood-free methods, pseudo-marginal MCMC, model misspecification, whitening transformation.
Monte Carlo, likelihood-free methods, pseudo-marginal MCMC, model misspecification, whitening transformation.
Description
Bayesian synthetic likelihood (BSL; Price, Drovandi, Lee, and Nott 2018) is a popular
method for estimating the parameter posterior distribution for complex statistical models
and stochastic processes that possess a computationally intractable likelihood function.
Instead of evaluating the likelihood, BSL approximates the likelihood of a judiciously chosen summary statistic of the data via model simulation and density estimation. Compared
to alternative methods such as approximate Bayesian computation (ABC), BSL requires
little tuning and requires less model simulations than ABC when the chosen summary
statistic is high-dimensional. The original synthetic likelihood relies on a multivariate
normal approximation of the intractable likelihood, where the mean and covariance are
estimated by simulation. An extension of BSL considers replacing the sample covariance
with a penalized covariance estimator to reduce the number of required model simulations. Further, a semi-parametric approach has been developed to relax the normality
assumption. Finally, another extension of BSL aims to develop a more robust synthetic
likelihood estimator while acknowledging there might be model misspecification. In this
paper, we present the R package BSL that amalgamates the aforementioned methods and
more into a single, easy-to-use and coherent piece of software. The package also includes
several examples to illustrate use of the package and the utility of the methods.
method for estimating the parameter posterior distribution for complex statistical models
and stochastic processes that possess a computationally intractable likelihood function.
Instead of evaluating the likelihood, BSL approximates the likelihood of a judiciously chosen summary statistic of the data via model simulation and density estimation. Compared
to alternative methods such as approximate Bayesian computation (ABC), BSL requires
little tuning and requires less model simulations than ABC when the chosen summary
statistic is high-dimensional. The original synthetic likelihood relies on a multivariate
normal approximation of the intractable likelihood, where the mean and covariance are
estimated by simulation. An extension of BSL considers replacing the sample covariance
with a penalized covariance estimator to reduce the number of required model simulations. Further, a semi-parametric approach has been developed to relax the normality
assumption. Finally, another extension of BSL aims to develop a more robust synthetic
likelihood estimator while acknowledging there might be model misspecification. In this
paper, we present the R package BSL that amalgamates the aforementioned methods and
more into a single, easy-to-use and coherent piece of software. The package also includes
several examples to illustrate use of the package and the utility of the methods.
Creator
Ziwen An
Source
https://www.jstatsoft.org/article/view/v101i11
Publisher
Queensland University
of Technology
of Technology
Date
January 2022
Contributor
Fajar bagus W
Format
PDF
Language
English
Type
Text
Files
Collection
Citation
Ziwen An, “BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood,” Repository Horizon University Indonesia, accessed April 12, 2025, https://repository.horizon.ac.id/items/show/8245.