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BSL (version 3.0.0)
Bayesian Synthetic Likelihood
Description
Bayesian synthetic likelihood (BSL, Price et al. (2018) )
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) ) uses
an unbiased estimator to the normal density. A semi-parametric version
of BSL (semiBSL, An et al. (2018) ) 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)
). Extensions to this package are
planned.