Bayesian Synthetic Likelihood
Bayesian synthetic likelihood (BSL, Price et al. (2018) <10.1080>)
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 three methods (BSL, uBSL and semiBSL) and two
shrinkage estimations (graphical lasso and Warton's estimation).
uBSL (Price et al. (2018) <10.1080>) uses
an unbiased estimator to the normal density. A semi-parametric version
of BSL (semiBSL, An et al. (2018) <1809.05800>) is more robust
to non-normal summary statistics. Shrinkage estimations can help to
bring down the number of simulations when the dimension of the summary
statistic is high (e.g., BSLasso, An et al. (2019)
<10.1080>). Extensions to this package are