publications

publications by categories in reversed chronological order. generated by jekyll-scholar.

2026

  1. arXiv
    Minimum Distance Summaries for Robust Neural Posterior Estimation
    Sherman Khoo, Dennis Prangle, Song Liu, and 1 more author
    arXiv preprint arXiv:2602.09161, 2026
    arXiv
  2. Bayesian emulation of geotechnical deterioration curves using quadratic and B-spline hierarchical models
    Jordan L. Oakley, Aleksandra Svalova, Peter Helm, and 4 more authors
    Journal of the Royal Statistical Society Series C: Applied Statistics, 2026

2025

  1. arXiv
    A Comparison of Kernels for ABC-SMC
    Dennis Prangle, Cecilia Viscardi, and Sammy Ragy
    arXiv preprint arXiv:2511.06351, 2025
    arXiv
  2. From climate risk to action: Analysing adaptation decision robustness under uncertainty
    Cecina Babich Morrow, Laura Dawkins, Francesca Pianosi, and 2 more authors
    Climate Risk Management, 2025
  3. ICML
    Flexible Tails for Normalizing Flows
    Tennessee Hickling, and Dennis Prangle
    In International Conference on Machine Learning, 2025
    arXiv

2024

  1. arXiv
    Optimal combination of composite likelihoods using approximate Bayesian computation with application to state-space models
    Wentao Li, Rosabeth White, and Dennis Prangle
    arXiv preprint, 2024
    arXiv
  2. Stat. Sci.
    Editorial: Bayesian Computations in the 21st Century
    Christian Robert, and Dennis Prangle
    Statistical Science, 2024
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2023

  1. JCGS
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    Distilling importance sampling for likelihood-free inference
    Dennis Prangle, and Cecilia Viscardi
    Journal of Computational and Graphical Statistics, 2023
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2022

  1. Bayesian Anal.
    Bayesian experimental design without posterior calculations: an adversarial approach
    Sophie Harbisher, Colin S. Gillespie, and Dennis Prangle
    Bayesian Analysis, 2022
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2021

  1. DCE
    Emulating computer experiments of transport infrastructure slope stability using Gaussian processes and Bayesian inference
    Aleksandra Svalova, Peter Helm, Dennis Prangle, and 3 more authors
    Data Centric Engineering, 2021
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  2. UAI
    The neural moving average model for scalable variational inference of state space models
    Tom Ryder, Andrew Golightly, Isaac Matthews, and 1 more author
    In Uncertainty in Artificial Intelligence, 2021
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  3. Bayesian Anal.
    Ensemble MCMC: Accelerating pseudo-marginal MCMC for state space models using the ensemble Kalman filter
    Chris Drovandi, Richard Everitt, Andrew Golightly, and 1 more author
    Bayesian Analysis, 2021
    arXiv
  4. AISTATS
    Measure transport with kernel Stein discrepancy
    Matthew Fisher, Tui Nolan, Matthew Graham, and 2 more authors
    In International Conference on Artificial Intelligence and Statistics, 2021
    arXiv

2020

  1. AISTATS
    Black-box inference for non-linear latent force models
    Wil O. C. Ward, Tom Ryder, Dennis Prangle, and 1 more author
    In International Conference on Artificial Intelligence and Statistics, 2020
    arXiv
  2. R Journal
    gk: An R Package for the g-and-k and generalised g-and-h Distributions
    Dennis Prangle
    The R Journal, 2020
    arXiv

2019

  1. RICAM
    Optimality criteria for probabilistic numerical methods
    Chris J. Oates, Jon Cockayne, Dennis Prangle, and 2 more authors
    In RICAM workshop proceedings, 2019
    arXiv

2018

  1. NeurIPS-W
    Black-box autoregressive density estimation for state-space models
    Thomas Ryder, Andrew Golightly, A. Stephen McGough, and 1 more author
    NeurIPS workshop paper, 2018
    arXiv
  2. CSDA
    Recalibration: A post-processing method for approximate Bayesian computation
    Guilherme Rodrigues, Dennis Prangle, and Scott Sisson
    Computational Statistics & Data Analysis, 2018
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  3. Handbook
    Summary statistics in Approximate Bayesian Computation
    Dennis Prangle
    In Handbook of ABC, 2018
    arXiv
  4. ICML
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    Black-box variational inference for stochastic differential equations
    Thomas Ryder, Andrew Golightly, A. Stephen McGough, and 1 more author
    In International Conference on Machine Learning, 2018
    arXiv
  5. Ecol. Appl.
    Taking error into account when fitting models using Approximate Bayesian Computation
    Elske Vaart, Dennis Prangle, and Richard M. Sibly
    Ecological Applications, 2018
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  6. Stat. Comput.
    A rare event approach to high dimensional Approximate Bayesian Computation
    Dennis Prangle, Richard G. Everitt, and Theodore Kypraios
    Statistics and Computing, 2018
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2017

  1. Bayesian Anal.
    adapting_ABC_distance.jpg
    Adapting the ABC distance function
    Dennis Prangle
    Bayesian Analysis, 2017
    HTML Code
  2. Math. Biosci.
    A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation
    Theodore Kypraios, Peter Neal, and Dennis Prangle
    Mathematical Biosciences, 2017
    HTML Code

2016

  1. Tech. Rep.
    An ABC interpretation of the multiple auxiliary variable method
    Dennis Prangle, and Richard G. Everitt
    2016
    arXiv
  2. Stat. Comput.
    Lazy ABC
    Dennis Prangle
    Statistics and Computing, 2016
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2015

  1. R Journal
    abctools: an R package for tuning approximate Bayesian computation analyses
    Matthew Nunes, and Dennis Prangle
    The R Journal, 2015
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  2. Am. J. Phys. Anthropol.
    The identification of individuals of advanced age using degeneration of the sternal end of the clavicle
    Ceri G. Falys, and Dennis Prangle
    American Journal of Physical Anthropology, 2015
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2014

  1. NeurIPS-W
    Lazier ABC
    Dennis Prangle
    NeurIPS workshop paper, 2014
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  2. Aust. N. Z. J. Stat.
    Diagnostic tools of approximate Bayesian computation using the coverage property
    Dennis Prangle, Micheal Blum, Gordana Popovic, and 1 more author
    Australian & New Zealand Journal of Statistics, 2014
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  3. SAGMB
    Semi-automatic selection of summary statistics for ABC model choice
    Dennis Prangle, Paul Fearnhead, Murray P. Cox, and 2 more authors
    Statistical Applications in Genetics and Molecular Biology, 2014
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2013

  1. Stat. Sci.
    A comparative review of dimension reduction methods in approximate Bayesian computation
    Micheal Blum, Matt Nunes, Dennis Prangle, and 1 more author
    Statistical Science, 2013
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2012

  1. JRSSB
    Constructing summary statistics for approximate Bayesian computation: semi-automatic ABC
    Paul Fearnhead, and Dennis Prangle
    Journal of the Royal Statistical Society: Series B, 2012
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2011

  1. PhD thesis
    thesis.jpg
    Summary statistics and sequential methods for approximate Bayesian computation
    Dennis Prangle
    Lancaster University, 2011
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