Dennis Prangle

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Paul FearnheadProfessor of Statistics; Lancaster UniversityVerified email at lancs.ac.uk
Scott SissonProfessor of Statistics & Data Science, University of New South WalesVerified email at unsw.edu.au
Michael BlumOWKINVerified email at owkin.com
Matthew A. NunesReader in Statistics, University of BathVerified email at bath.ac.uk
Tom RyderNewcastle UniversityVerified email at ncl.ac.uk
Andrew GolightlyReader, Newcastle UniversityVerified email at ncl.ac.uk
Richard G. EverittUniversity of WarwickVerified email at warwick.ac.uk
Theodore KypraiosProfessor of Statistics, University of NottinghamVerified email at nottingham.ac.uk
A.S.McGoughNewcastle UniversityVerified email at newcastle.ac.uk
Patrick BiggsAssoc. Prof. of Genomics & Computational Biology, Massey UniversityVerified email at massey.ac.nz
Murray CoxProfessor of Computational Biology; Deputy Head, School of Fundamental SciencesVerified email at massey.ac.nz
Nigel FrenchDistinguished Professor of Veterinary Public Health, Massey University.Verified email at massey.ac.nz
Peter NealProfessor of Statistics, University of NottinghamVerified email at nottingham.ac.uk
Gordana PopovicUNSW SydneyVerified email at unsw.edu.au
Richard SiblyUniversity of ReadingVerified email at reading.ac.uk
Elske van der VaartAssistant Professor, Tilburg University, The NetherlandsVerified email at uvt.nl
Guilherme RodriguesSenior Lecturer, University of BrasiliaVerified email at unb.br
Chris. J. OatesChair in Statistics, Newcastle UniversityVerified email at ncl.ac.uk
Philip MaybankNumerical software developer, Numerical Algorithms Group Ltd (NAG)Verified email at nag.co.uk
Christopher DrovandiProfessor of Statistics, Queensland University of TechnologyVerified email at qut.edu.au

Dennis Prangle

Newcastle University

Verified email at newcastle.ac.uk

ABC variational inference SDEs experimental design


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Constructing summary statistics for approximate Bayesian computation: semi‐automatic approximate Bayesian computation P Fearnhead, D Prangle Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2012	561	2012
A comparative review of dimension reduction methods in approximate Bayesian computation MGB Blum, MA Nunes, D Prangle, SA Sisson Arxiv preprint arXiv:1202.3819, 2012	315	2012
Semi-automatic selection of summary statistics for ABC model choice D Prangle, P Fearnhead, MP Cox, PJ Biggs, NP French Statistical applications in genetics and molecular biology 13 (1), 67-82, 2014	63	2014
Adapting the ABC distance function D Prangle Bayesian Analysis 12 (1), 289-309, 2017	59	2017
Lazy ABC D Prangle Statistics and Computing 26 (1-2), 171-185, 2016	52	2016
A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation T Kypraios, P Neal, D Prangle Mathematical Biosciences 287, 42-53, 2017	47	2017
Diagnostic tools for approximate Bayesian computation using the coverage property D Prangle, MGB Blum, G Popovic, SA Sisson Australian & New Zealand Journal of Statistics 56 (4), 309-329, 2014	47	2014
abctools: an R package for tuning Approximate Bayesian Computation analyses MA Nunes, D Prangle The R Journal 7 (2), 189-205, 2015	46	2015
Summary statistics D Prangle Handbook of Approximate Bayesian Computation, 125-152, 2018	40*	2018
Estimating age of mature adults from the degeneration of the sternal end of the clavicle CG Falys, D Prangle American journal of physical anthropology 156 (2), 203-214, 2015	35	2015
Black-box Variational Inference for Stochastic Differential Equations T Ryder, A Golightly, AS McGough, D Prangle arXiv preprint arXiv:1802.03335, 2018	28	2018
Recalibration: A post-processing method for approximate Bayesian computation GS Rodrigues, D Prangle, SA Sisson Computational Statistics & Data Analysis 126, 53-66, 2018	13	2018
A rare event approach to high-dimensional approximate Bayesian computation D Prangle, RG Everitt, T Kypraios Statistics and Computing 28 (4), 819-834, 2018	13	2018
Taking Error Into Account When Fitting Models Using Approximate Bayesian Computation E van der Vaart, D Prangle, RM Sibly Ecological Applications, 2017	13	2017
gk: An R Package for the g-and-k and generalised g-and-h Distributions D Prangle arXiv preprint arXiv:1706.06889, 2017	9	2017
Summary statistics and sequential methods for approximate Bayesian computation D Prangle Lancaster University, 2011	8	2011
Ensemble MCMC: accelerating pseudo-marginal MCMC for state space models using the ensemble kalman filter C Drovandi, RG Everitt, A Golightly, D Prangle Bayesian Analysis, 2020	4	2020
Distilling importance sampling D Prangle arXiv preprint arXiv:1910.03632, 2019	4	2019
Marginal sequential Monte Carlo for doubly intractable models RG Everitt, D Prangle, P Maybank, M Bell arXiv preprint arXiv:1710.04382, 2017	4	2017
Optimality criteria for probabilistic numerical methods CJ Oates, J Cockayne, D Prangle, TJ Sullivan, M Girolami	3	2019

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