Imperial College London


Faculty of Natural SciencesDepartment of Mathematics

Professor of Computational Mathematics



+44 (0)20 7594 3468colin.cotter




755Huxley BuildingSouth Kensington Campus






BibTex format

author = {Cotter, C and Cotter, S and Russell, P},
doi = {10.1137/17M1114867},
journal = {SIAM/ASA Journal on Uncertainty Quantification},
pages = {444--471},
title = {Ensemble transport adaptive importance sampling},
url = {},
volume = {7},
year = {2019}

RIS format (EndNote, RefMan)

AB - Markov chain Monte Carlo methods are a powerful and commonly used family ofnumerical methods for sampling from complex probability distributions. As applications of thesemethods increase in size and complexity, the need for efficient methods increases. In this paper, wepresent a particle ensemble algorithm. At each iteration, an importance sampling proposal distri-bution is formed using an ensemble of particles. A stratified sample is taken from this distributionand weighted under the posterior, a state-of-the-art ensemble transport resampling method is thenused to create an evenly weighted sample ready for the next iteration. We demonstrate that thisensemble transport adaptive importance sampling (ETAIS) method outperforms MCMC methodswith equivalent proposal distributions for low dimensional problems, and in fact shows better thanlinear improvements in convergence rates with respect to the number of ensemble members. We alsointroduce a new resampling strategy, multinomial transformation (MT), which while not as accurateas the ensemble transport resampler, is substantially less costly for large ensemble sizes, and canthen be used in conjunction with ETAIS for complex problems. We also focus on how algorithmicparameters regarding the mixture proposal can be quickly tuned to optimise performance. In partic-ular, we demonstrate this methodology’s superior sampling for multimodal problems, such as thosearising from inference for mixture models, and for problems with expensive likelihoods requiring thesolution of a differential equation, for which speed-ups of orders of magnitude are demonstrated.Likelihood evaluations of the ensemble could be computed in a distributed manner, suggesting thatthis methodology is a good candidate for parallel Bayesian computations.
AU - Cotter,C
AU - Cotter,S
AU - Russell,P
DO - 10.1137/17M1114867
EP - 471
PY - 2019///
SN - 2166-2525
SP - 444
TI - Ensemble transport adaptive importance sampling
T2 - SIAM/ASA Journal on Uncertainty Quantification
UR -
UR -
VL - 7
ER -