penbsl

print.penbsl

plot.penbsl

A ``penbsl" class object, typically the output of function <code><a rd-options="" href="/link/selectPenalty?package=BSL&version=3.0.0" data-mini-rdoc="BSL::selectPenalty">selectPenalty</a></code>.

digits

A logical indicator whether the x-axis (penalty) should be log transformed. The
default is <code>TRUE</code>.

logscale

Two functions (print and plot) are provided for class ``penbsl".

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.

Ziwen An

Bayesian Synthetic Likelihood

Leah F. South

Christopher C. Drovandi

penbsl function

A ``penbsl" class object, typically the output of function <code><a rd-options='' href='selectPenalty'>selectPenalty</a></code>.

penbsl: S3 reference class of the result from tuning to select the optimal penalty for BSLasso

Description

Usage

Arguments