BSL (2.0.0)

Bayesian Synthetic Likelihood.

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 BSL, BSLasso and semiBSL. BSL with graphical lasso (BSLasso, An et al. (2018) ) is computationally more efficient when the dimension of the summary statistic is high. A semi-parametric version of BSL (semiBSL, An et al. (2018) ) is more robust to non-normal summary statistics. Extensions to this package are planned.

Maintainer: Ziwen An
Author(s): Ziwen An [aut, cre] (<>), Leah F. South [aut] (<>), Christopher C. Drovandi [aut] (<>)

License: GPL (>= 2)

Uses: coda, copula, cvTools, foreach, ggplot2, glasso, gridExtra, MASS, mvtnorm, Rcpp, doParallel, elliplot

Released 11 months ago.