{"name":"r-zvcv","version":"2.1.0","synopsis":{"source":"Zero-Variance Control Variates","html":"

Zero-Variance Control Variates

","plain":"Zero-Variance Control Variates\n\n","locale":"en_US.UTF-8"},"description":{"source":"@dfn{Zero-variance control variates} (ZV-CV) is a post-processing method\nto reduce the variance of Monte Carlo estimators of expectations using the\nderivatives of the log target. Once the derivatives are available, the only\nadditional computational effort is in solving a linear regression problem.\nThis method has been extended to higher dimensions using regularisation. This\npackage can be used to easily perform ZV-CV or regularised ZV-CV when a set of\nsamples, derivatives and function evaluations are available. Additional\nfunctions for applying ZV-CV to two estimators for the normalising constant of\nthe posterior distribution in Bayesian statistics are also supplied.","html":"

Zero-variance control variates (ZV-CV) is a post-processing method to reduce the variance of Monte Carlo estimators of expectations using the derivatives of the log target. Once the derivatives are available, the only additional computational effort is in solving a linear regression problem. This method has been extended to higher dimensions using regularisation. This package can be used to easily perform ZV-CV or regularised ZV-CV when a set of samples, derivatives and function evaluations are available. Additional functions for applying ZV-CV to two estimators for the normalising constant of the posterior distribution in Bayesian statistics are also supplied.

","plain":"\"Zero-variance control variates\" (ZV-CV) is a post-processing method to reduce\nthe variance of Monte Carlo estimators of expectations using the derivatives of\nthe log target. Once the derivatives are available, the only additional\ncomputational effort is in solving a linear regression problem. This method has\nbeen extended to higher dimensions using regularisation. This package can be\nused to easily perform ZV-CV or regularised ZV-CV when a set of samples,\nderivatives and function evaluations are available. Additional functions for\napplying ZV-CV to two estimators for the normalising constant of the posterior\ndistribution in Bayesian statistics are also supplied.\n\n","locale":"en_US.UTF-8"},"home-page":"https://cran.r-project.org/web/packages/ZVCV/","derivations":[{"system":"x86_64-linux","target":"","derivation":"/gnu/store/fn5hj980c0hw9ncysfmy08z1566hdrgp-r-zvcv-2.1.0.drv"},{"system":"mips64el-linux","target":"","derivation":"/gnu/store/ar58w61bwzlfwsraajr4w54ki4pkm8a3-r-zvcv-2.1.0.drv"},{"system":"i686-linux","target":"","derivation":"/gnu/store/mzzmbnrw1nbgrir8vqm1qd58dcswrvhw-r-zvcv-2.1.0.drv"},{"system":"i586-gnu","target":"","derivation":"/gnu/store/zhqqrp97b58wk2cc6x25fmgsxwzn5w4c-r-zvcv-2.1.0.drv"},{"system":"armhf-linux","target":"","derivation":"/gnu/store/46k31qm5fi176vqg13ca2zx6pab7a2lz-r-zvcv-2.1.0.drv"},{"system":"aarch64-linux","target":"","derivation":"/gnu/store/nz9yw7zv5jvm0yphqqgxd4g3gxz4vvvv-r-zvcv-2.1.0.drv"}]}