Installation
About
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 four methods (BSL, uBSL, semiBSL and BSLmisspec) and two shrinkage estimators (graphical lasso and Warton's estimator). 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)
Citation | BSL citation info |
Key Metrics
Downloads
Yesterday | 25 0% |
Last 7 days | 73 -13% |
Last 30 days | 340 -11% |
Last 90 days | 1.000 -11% |
Last 365 days | 4.154 -14% |