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ZVCV (version 1.0.0)

Zero-Variance Control Variates

Description

Zero-variance control variates (ZV-CV, Mira et al. (2013) ) 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. Recently, this method has been extended to higher dimensions using regularisation (South et al., 2018 ). 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.

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Version

Install

install.packages('ZVCV')

Monthly Downloads

154

Version

1.0.0

License

GPL (>= 2)

Maintainer

Leah F. South

Last Published

January 24th, 2019

Functions in ZVCV (1.0.0)

getX

The function getX is used to get the matrix of covariates for the regression based on a specified polynomial order.
helper_functions

Useful helper functions
Expand_Temperatures

Adjusting the temperature schedule
ZVCV_package

Zero-Variance Control Variates
VDP

Example of estimation using SMC
evidence

Evidence estimation with ZV-CV
zvcv

ZV-CV for general expectations