Imperial College London

Professor Nick Heard

Faculty of Natural SciencesDepartment of Mathematics

Chair in Statistics



+44 (0)20 7594 1490n.heard Website




543Huxley BuildingSouth Kensington Campus






BibTex format

author = {Heard, NA and Turcotte, MJM},
doi = {10.1007/s11222-015-9595-0},
journal = {Statistics and Computing},
pages = {1147--1161},
title = {Convergence of Monte Carlo distribution estimates from rival samplers},
url = {},
volume = {26},
year = {2015}

RIS format (EndNote, RefMan)

AB - It is often necessary to make sampling-based statistical inference about many probability distributions in parallel. Given a finite computational resource, this article addresses how to optimally divide sampling effort between the samplers of the different distributions. Formally approaching this decision problem requires both the specification of an error criterion to assess how well each group of samples represent their underlying distribution, and a loss function to combine the errors into an overall performance score. For the first part, a new Monte Carlo divergence error criterion based on Jensen–Shannon divergence is proposed. Using results from information theory, approximations are derived for estimating this criterion for each target based on a single run, enabling adaptive sample size choices to be made during sampling.
AU - Heard,NA
AU - Turcotte,MJM
DO - 10.1007/s11222-015-9595-0
EP - 1161
PY - 2015///
SN - 1573-1375
SP - 1147
TI - Convergence of Monte Carlo distribution estimates from rival samplers
T2 - Statistics and Computing
UR -
UR -
VL - 26
ER -