Control functionals for Monte Carlo integration
- Publication Type:
- Journal Article
- Journal of the Royal Statistical Society. Series B: Statistical Methodology, 2017, 79 (3), pp. 695 - 718
- Issue Date:
Files in This Item:
|Oates_et_al-2017-Journal_of_the_Royal_Statistical_Society-_Series_B_(Statistical_Methodology).pdf||Published Version||800.35 kB|
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
© 2016 Royal Statistical Society A non-parametric extension of control variates is presented. These leverage gradient information on the sampling density to achieve substantial variance reduction. It is not required that the sampling density be normalized. The novel contribution of this work is based on two important insights: a trade-off between random sampling and deterministic approximation and a new gradient-based function space derived from Stein's identity. Unlike classical control variates, our estimators improve rates of convergence, often requiring orders of magnitude fewer simulations to achieve a fixed level of precision. Theoretical and empirical results are presented, the latter focusing on integration problems arising in hierarchical models and models based on non-linear ordinary differential equations.
Please use this identifier to cite or link to this item: