Dennis Prangle
Dennis Prangle
Verified email at newcastle.ac.uk
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
5132012
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
2852012
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
622014
Adapting the ABC distance function
D Prangle
Bayesian Analysis 12 (1), 289-309, 2017
462017
Lazy ABC
D Prangle
Statistics and Computing 26 (1-2), 171-185, 2016
462016
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
432014
abctools: an R package for tuning Approximate Bayesian Computation analyses
MA Nunes, D Prangle
The R Journal 7 (2), 189-205, 2015
402015
Summary statistics
D Prangle
Handbook of Approximate Bayesian Computation, 125-152, 2018
32*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
322015
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
302017
Black-box Variational Inference for Stochastic Differential Equations
T Ryder, A Golightly, AS McGough, D Prangle
arXiv preprint arXiv:1802.03335, 2018
172018
Recalibration: A post-processing method for approximate Bayesian computation
GS Rodrigues, D Prangle, SA Sisson
Computational Statistics & Data Analysis 126, 53-66, 2018
112018
Taking Error Into Account When Fitting Models Using Approximate Bayesian Computation
E van der Vaart, D Prangle, RM Sibly
Ecological Applications, 2017
102017
A rare event approach to high-dimensional approximate Bayesian computation
D Prangle, RG Everitt, T Kypraios
Statistics and Computing 28 (4), 819-834, 2018
92018
gk: An R Package for the g-and-k and generalised g-and-h Distributions
D Prangle
arXiv preprint arXiv:1706.06889, 2017
92017
Summary statistics and sequential methods for approximate Bayesian computation
D Prangle
Lancaster University, 2011
82011
Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter
C Drovandi, RG Everitt, A Golightly, D Prangle
arXiv preprint arXiv:1906.02014, 2019
32019
Optimality criteria for probabilistic numerical methods
C Oates, J Cockayne, D Prangle, TJ Sullivan, M Girolami
arXiv preprint arXiv:1901.04326, 2019
32019
Lazier ABC
D Prangle
arXiv preprint arXiv:1501.05144, 2015
32015
Variational Bridge Constructs for Grey Box Modelling with Gaussian Processes
WOC Ward, T Ryder, D Prangle, MA Álvarez
arXiv preprint arXiv:1906.09199, 2019
22019
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Articles 1–20