.Object

<p>Generate the generic simulation and summary statistics function for <code>n</code> simulations
and fixed <code>theta</code> (for internal use).</p>

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 three methods (BSL, uBSL and semiBSL) and two
shrinkage estimations (graphical lasso and Warton's estimation).
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. 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)
<doi:10.1080/10618600.2018.1537928>). Extensions to this package are
planned.

fn: Functions to be used in bsl (for internal use)

Description

Usage

Arguments