Dennis Prangle 个人学术档案

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	总计	2019 年至今
引用	2243	1373
h 指数	17	17
i10 指数	22	22

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Paul FearnheadProfessor of Statistics; Lancaster University在 lancs.ac.uk 的电子邮件经过验证
Scott SissonProfessor of Statistics & Data Science, University of New South Wales在 unsw.edu.au 的电子邮件经过验证
Tom RyderQualcomm在 qti.qualcomm.com 的电子邮件经过验证
Michael BlumSeqOne在 seqone.com 的电子邮件经过验证
Matthew A. NunesReader in Statistics, University of Bath在 bath.ac.uk 的电子邮件经过验证
Andrew GolightlyDurham University, UK在 durham.ac.uk 的电子邮件经过验证
Richard G. EverittUniversity of Warwick在 warwick.ac.uk 的电子邮件经过验证
A.S.McGoughNewcastle University在 newcastle.ac.uk 的电子邮件经过验证
Theodore KypraiosProfessor of Statistics, University of Nottingham在 nottingham.ac.uk 的电子邮件经过验证
Peter NealProfessor of Statistics, University of Nottingham在 nottingham.ac.uk 的电子邮件经过验证
Paolo MissierProfessor of Big Data Engineering and Analytics在 acm.org 的电子邮件经过验证
Vladimiro González-ZelayaLecturer at Universidad Panamericana在 up.edu.mx 的电子邮件经过验证
Patrick BiggsProfessor of Genomics & Computational Biology, Massey University在 massey.ac.nz 的电子邮件经过验证
Murray CoxBiological Data Analytics在 genomicus.com 的电子邮件经过验证
Nigel FrenchDistinguished Professor of Infectious Disease Epidemiology and Public Health, Massey University.在 massey.ac.nz 的电子邮件经过验证
Gordana PopovicUNSW Sydney在 unsw.edu.au 的电子邮件经过验证
Mohamed RouainiaProfessor of Computational Geomechanics, Newcastle University在 ncl.ac.uk 的电子邮件经过验证
Darren WilkinsonProfessor of Statistics, Durham University, U.K.在 durham.ac.uk 的电子邮件经过验证
Peter HelmPostdoctoral Researcher, School of Engineering, Newcastle University在 newcastle.ac.uk 的电子邮件经过验证
Chris. J. OatesNewcastle University在 ncl.ac.uk 的电子邮件经过验证

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Dennis Prangle

University of Bristol

在 bristol.ac.uk 的电子邮件经过验证

ABC variational inference SDEs experimental design


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
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	791	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	450	2012
Adapting the ABC distance function D Prangle Bayesian Analysis 12 (1), 289-309, 2017	119	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	101	2017
Summary statistics D Prangle Handbook of Approximate Bayesian Computation, 125-152, 2018	90	2018
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	90	2014
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	85	2014
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	71	2015
Black-box variational inference for stochastic differential equations T Ryder, A Golightly, AS McGough, D Prangle International Conference on Machine Learning, 4423-4432, 2018	68	2018
Lazy ABC D Prangle Statistics and Computing 26 (1-2), 171-185, 2016	62	2016
abctools: an R package for tuning Approximate Bayesian Computation analyses MA Nunes, D Prangle The R Journal 7 (2), 189-205, 2015	62	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	32	2018
Recalibration: A post-processing method for approximate Bayesian computation GS Rodrigues, D Prangle, SA Sisson Computational Statistics & Data Analysis 126, 53-66, 2018	26	2018
gk: An R Package for the g-and-k and generalised g-and-h Distributions D Prangle arXiv preprint arXiv:1706.06889, 2017	26	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	20	2020
Taking Error Into Account When Fitting Models Using Approximate Bayesian Computation E van der Vaart, D Prangle, RM Sibly Ecological Applications, 2017	19	2017
Distilling Importance Sampling for Likelihood Free Inference D Prangle, C Viscardi Journal of Computational and Graphical Statistics, 1-22, 2023	17*	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	16	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	15	2020
Parametrised data sampling for fairness optimisation V Zelaya, P Missier, D Prangle KDD XAI, 2019	14	2019

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