BSL: Bayesian Synthetic Likelihood

Bayesian synthetic likelihood (BSL, Price et al. (2018) <doi:10.1080/10618600.2017.1302882>) is an alternative to standard, non-parametric approximate Bayesian computation (ABC). BSL assumes a multivariate normal distribution for the summary statistic likelihood and it is suitable when the distribution of the model summary statistics is sufficiently regular. This package provides a Metropolis Hastings Markov chain Monte Carlo implementation of BSL, BSLasso and semiBSL. BSL with graphical lasso (BSLasso, An et al. (2018) <>) is computationally more efficient when the dimension of the summary statistic is high. A semi-parametric version of BSL (semiBSL, An et al. (2018) <arXiv:1809.05800>) is more robust to non-normal summary statistics. Extensions to this package are planned.

Package details

AuthorZiwen An [aut, cre] (<>), Leah F. South [aut] (<>), Christopher C. Drovandi [aut] (<>)
MaintainerZiwen An <[email protected]>
LicenseGPL (>= 2)
Package repositoryView on CRAN
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BSL documentation built on Jan. 16, 2019, 9:07 a.m.