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) ), regularised ZV-CV (South et al., 2018 ), control functionals (CF, Oates et al. (2017) ) and semi-exact control functionals (SECF, South et al., 2020 ). 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.


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2.1.1 by Leah F. South, 7 months ago

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Authors: Leah F. South [aut, cre]

Documentation:   PDF Manual  

GPL (>= 2) license

Imports Rcpp, glmnet, abind, mvtnorm, stats, Rlinsolve, magrittr, dplyr

Suggests partitions, ggplot2, ggthemes

Linking to Rcpp, RcppArmadillo, BH

See at CRAN