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) <https://eprints.qut.edu.au/102263/>) 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.
|Author||Ziwen An [aut, cre] (<https://orcid.org/0000-0002-9947-5182>), Leah F. South [aut] (<https://orcid.org/0000-0002-5646-2963>), Christopher C. Drovandi [aut] (<https://orcid.org/0000-0001-9222-8763>)|
|Maintainer||Ziwen An <[email protected]>|
|License||GPL (>= 2)|
|Package repository||View on CRAN|
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