ZVCV: Zero-Variance Control Variates
Stein control variates can be used to improve Monte Carlo estimates of expectations when the derivatives of the log target are available. This package implements a variety of such methods, including zero-variance control variates (ZV-CV, Mira et al. (2013) <doi:10.1007/s11222-012-9344-6>), regularised ZV-CV (South et al., 2018 <arXiv:1811.05073>), control functionals (CF, Oates et al. (2017) <doi:10.1111/rssb.12185>) and semi-exact control functionals (SECF, South et al., 2020 <arXiv:2002.00033>). ZV-CV is a parametric approach that is exact for (low order) polynomial integrands with Gaussian targets. CF is a non-parametric alternative that offers better than the standard Monte Carlo convergence rates. SECF has both a parametric and a non-parametric component and it offers the advantages of both for an additional computational cost. Functions for applying ZV-CV and CF to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied in this package. The basic requirements for using the package are a set of samples, derivatives and function evaluations.
||Rcpp (≥ 0.11.0), glmnet, abind, mvtnorm, stats, Rlinsolve, magrittr, dplyr
||Rcpp, RcppArmadillo, BH
||partitions, ggplot2, ggthemes
||Leah F. South
||Leah F. South <leah.south at hdr.qut.edu.au>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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