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 four methods (BSL, uBSL,
semiBSL and BSLmisspec) and two shrinkage estimators (graphical
lasso and Warton's estimator). uBSL (Price et al. (2018)
<doi:10.1080/10618600.2017.1302882>) uses an unbiased estimator
to the normal density. A semi-parametric version of BSL
(semiBSL, An et al. (2018) <arXiv:1809.05800>) is more robust
to non-normal summary statistics. BSLmisspec (Frazier et al.
2019 <arXiv:1904.04551>) estimates the Gaussian synthetic
likelihood whilst acknowledging that there may be
incompatibility between the model and the observed summary
statistic. Shrinkage estimation can help to decrease the number
of model simulations when the dimension of the summary
statistic is high (e.g., BSLasso, An et al. (2019)
<doi:10.1080/10618600.2018.1537928>). Extensions to this
package are planned. For a journal article describing how to
use this package, see An et al. (2022)
<doi:10.18637/jss.v101.i11>.
