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 BlumSeqOneVerified email at seqone.com
Matthew A. NunesReader in Statistics, University of BathVerified email at bath.ac.uk
Tom RyderQualcommVerified email at qti.qualcomm.com
Andrew GolightlyAssociate Professor, Durham UniversityVerified email at durham.ac.uk
Richard G. EverittUniversity of WarwickVerified email at warwick.ac.uk
A.S.McGoughNewcastle UniversityVerified email at newcastle.ac.uk
Theodore KypraiosProfessor of Statistics, University of NottinghamVerified email at nottingham.ac.uk
Peter NealProfessor of Statistics, University of NottinghamVerified email at nottingham.ac.uk
Patrick BiggsProfessor of Genomics & Computational Biology, Massey UniversityVerified email at massey.ac.nz
Murray CoxBiological Data AnalyticsVerified email at genomicus.com
Nigel FrenchDistinguished Professor of Infectious Disease Epidemiology and Public Health, Massey University.Verified email at massey.ac.nz
Gordana PopovicUNSW SydneyVerified email at unsw.edu.au
Chris. J. OatesNewcastle UniversityVerified email at ncl.ac.uk
Wil O C WardDepartment of Civil and Structural Engineering, University of SheffieldVerified email at sheffield.ac.uk
Mauricio A. ÁlvarezUniversity of ManchesterVerified email at manchester.ac.uk
Guilherme RodriguesSenior Lecturer, University of BrasiliaVerified email at unb.br
Rouainia MComputational Geomechanics, Newcastle UniversityVerified email at ncl.ac.uk
Darren WilkinsonProfessor of Statistics, Durham University, U.K.Verified email at durham.ac.uk

Dennis Prangle

Bristol University

Verified email at bristol.ac.uk

ABC variational inference SDEs experimental design


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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	712	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	412	2012
Adapting the ABC distance function D Prangle Bayesian Analysis 12 (1), 289-309, 2017	102	2017
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	88	2017
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	81	2014
Summary statistics D Prangle Handbook of Approximate Bayesian Computation, 125-152, 2018	76*	2018
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	69	2014
Black-box variational inference for stochastic differential equations T Ryder, A Golightly, AS McGough, D Prangle International Conference on Machine Learning, 4423-4432, 2018	60	2018
abctools: an R package for tuning Approximate Bayesian Computation analyses MA Nunes, D Prangle The R Journal 7 (2), 189-205, 2015	60	2015
Lazy ABC D Prangle Statistics and Computing 26 (1-2), 171-185, 2016	58	2016
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	51	2015
A rare event approach to high-dimensional approximate Bayesian computation D Prangle, RG Everitt, T Kypraios Statistics and Computing 28 (4), 819-834, 2018	31	2018
gk: An R Package for the g-and-k and generalised g-and-h Distributions D Prangle arXiv preprint arXiv:1706.06889, 2017	24	2017
Recalibration: A post-processing method for approximate Bayesian computation GS Rodrigues, D Prangle, SA Sisson Computational Statistics & Data Analysis 126, 53-66, 2018	21	2018
Taking Error Into Account When Fitting Models Using Approximate Bayesian Computation E van der Vaart, D Prangle, RM Sibly Ecological Applications, 2017	17	2017
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	16	2020
Distilling Importance Sampling for Likelihood Free Inference D Prangle, C Viscardi Journal of Computational and Graphical Statistics, 1-22, 2023	15*	2023
Measure transport with kernel Stein discrepancy M Fisher, T Nolan, M Graham, D Prangle, C Oates International Conference on Artificial Intelligence and Statistics, 1054-1062, 2021	11	2021
Black-Box Inference for Non-Linear Latent Force Models W Ward, T Ryder, D Prangle, M Alvarez International Conference on Artificial Intelligence and Statistics, 3088-3098, 2020	11	2020
Parametrised data sampling for fairness optimisation V Zelaya, P Missier, D Prangle KDD XAI, 2019	10	2019

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