From 862ae52ab919dfb002c9eac80f24d1cef1d199f0 Mon Sep 17 00:00:00 2001 From: Chris Oates Date: Wed, 14 Jul 2021 13:27:00 +0100 Subject: [PATCH] Removed Oates et al 2017, as not relevant --- .../research/bibliography/Quadrature.bib | 25 ------------------- 1 file changed, 25 deletions(-) diff --git a/docs/source/research/bibliography/Quadrature.bib b/docs/source/research/bibliography/Quadrature.bib index d3f8db2a4..09f657cc7 100644 --- a/docs/source/research/bibliography/Quadrature.bib +++ b/docs/source/research/bibliography/Quadrature.bib @@ -259,31 +259,6 @@ @InProceedings{gunter14-fast-bayesian-quadrature code = {https://github.com/OxfordML/wsabi} } - -@Article{oates14-contr-monte-carlo, - author = {Chris J. Oates and Mark Girolami and Nicolas Chopin}, - title = {Control functionals for Monte Carlo integration}, - journal = {arXiv preprint 1410.2392}, - year = 2014, - abstract = {This paper introduces a novel class of estimators for Monte - Carlo integration, that leverage gradient information in - order to provide the improved estimator performance demanded - by contemporary statistical applications. The proposed - estimators, called "control functionals", achieve sub-root-n - convergence and often require orders of magnitude fewer - simulations, compared with existing approaches, in order to - achieve a fixed level of precision. We focus on a particular - sub-class of estimators that permit an elegant analytic form - and study their properties, both theoretically and - empirically. Results are presented on Bayes-Hermite - quadrature, hierarchical Gaussian process models and - non-linear ordinary differential equation models, where in - each case our estimators are shown to offer state of the art - performance.}, - link = {http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-research/oates/control_functionals}, - file = {http://arxiv.org/pdf/1410.2392v1} -} - @ARTICLE{2015arXiv150405994S, author = {{S{\"a}rkk{\"a}}, S. and {Hartikainen}, J. and {Svensson}, L. and {Sandblom}, F.},