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.


BSL 0.1.0

  • Initial release
  • Includes BSL and BSLasso
  • Data and code for three examples: MA(2), multivariate G & K, and cell biology

Reference manual

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2.0.0 by Ziwen An, 8 days ago

Browse source code at

Authors: Ziwen An [aut, cre] , Leah F. South [aut] , Christopher C. Drovandi [aut]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports glasso, ggplot2, MASS, mvtnorm, copula, cvTools, graphics, gridExtra, foreach, coda, Rcpp, methods

Suggests elliplot, doParallel

Linking to Rcpp, RcppArmadillo

See at CRAN