Samuel Livingstone

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Mark GirolamiUniversity of CambridgeVerified email at cam.ac.uk
Simon ByrneCliMA project, California Institute of TechnologyVerified email at caltech.edu
Giacomo ZanellaBocconi UniversityVerified email at unibocconi.it
Christophe AndrieuProfessor of Statistics, University of BristolVerified email at bristol.ac.uk
Chris SherlockLancaster UniversityVerified email at lancaster.ac.uk
Tatiana XifaraVisiting Assistant Professor, University of California Santa CruzVerified email at ams.ucsc.edu
Michael F. FaulknerAssistant Professor, University of WarwickVerified email at warwick.ac.uk
Jim GriffinUniversity College LondonVerified email at ucl.ac.uk
Xitong LiangUCLVerified email at ucl.ac.uk
Dino SejdinovicProfessor of Statistical Machine Learning, University of AdelaideVerified email at adelaide.edu.au
Arthur GrettonGatsby Computational Neuroscience Unit and Google DeepmindVerified email at gatsby.ucl.ac.uk
Zoltan SzaboProfessor, LSEVerified email at lse.ac.uk
Heiko StrathmannResearch Scientist at DeepmindVerified email at deepmind.com
Gareth RobertsProfessor of Statistics, University of WarwickVerified email at warwick.ac.uk
Anthony LeeSchool of Mathematics, University of BristolVerified email at bristol.ac.uk
Jure VogrincUniversity of WarwickVerified email at warwick.ac.uk
Alberto CaronResearch Associate, The Alan Turing InstituteVerified email at ucl.ac.uk
Gianluca BaioUniversity College LondonVerified email at ucl.ac.uk
Luke HardcastlePhD student, Dept. Statistical Science, University College LondonVerified email at ucl.ac.uk
Claire BlackVerified email at ucl.ac.uk

Samuel Livingstone

Associate professor in mathematical statistics, University College London

Verified email at ucl.ac.uk - Homepage

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The geometric foundations of hamiltonian monte carlo MJ Betancourt, S Byrne, S Livingstone, M Girolami Bernoulli 23 (4A), 2257-2298, 2017	231	2017
Langevin diffusions and the Metropolis-adjusted Langevin algorithm T Xifara, C Sherlock, S Livingstone, S Byrne, M Girolami Statistics & Probability Letters 91, 14-19, 2014	161	2014
On the geometric ergodicity of Hamiltonian Monte Carlo S Livingstone, M Betancourt, S Byrne, M Girolami Bernoulli 25 (4A), 3109-3138, 2019	153	2019
Gradient-free Hamiltonian Monte Carlo with efficient kernel exponential families H Strathmann, D Sejdinovic, S Livingstone, Z Szabo, A Gretton Advances in Neural Information Processing Systems 28, 2015	93	2015
Kinetic energy choice in Hamiltonian/hybrid Monte Carlo S Livingstone, MF Faulkner, GO Roberts Biometrika 106 (2), 303-319, 2019	63	2019
The Barker proposal: combining robustness and efficiency in gradient-based MCMC S Livingstone, G Zanella Journal of the Royal Statistical Society. Series B, Statistical Methodology …, 2022	59*	2022
Peskun–Tierney ordering for Markovian Monte Carlo: beyond the reversible scenario C Andrieu, S Livingstone The Annals of Statistics 49 (4), 1958-1981, 2021	51*	2021
Information-geometric Markov chain Monte Carlo methods using diffusions S Livingstone, M Girolami Entropy 16 (6), 3074-3102, 2014	51	2014
A general perspective on the Metropolis-Hastings kernel C Andrieu, A Lee, S Livingstone arXiv preprint arXiv:2012.14881, 2020	23	2020
Geometric ergodicity of the Random Walk Metropolis with position-dependent proposal covariance S Livingstone Mathematics 9 (4), 341, 2021	15*	2021
Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms J Vogrinc, S Livingstone, G Zanella Biometrika, 2022	14	2022
Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable selection X Liang, S Livingstone, J Griffin Statistics and Computing 32 (5), 84, 2022	12	2022
A fresh take on 'Barker dynamics' for MCMC M Hird, S Livingstone, G Zanella International Conference on Monte Carlo and Quasi-Monte Carlo methods in …, 2022	9	2022
Adaptive mcmc for bayesian variable selection in generalised linear models and survival models X Liang, S Livingstone, J Griffin Entropy 25 (9), 1310, 2023	5	2023
Sampling algorithms in statistical physics: a guide for statistics and machine learning MF Faulkner, S Livingstone Statistical Science, 2023	5	2023
Quantifying the effectiveness of linear preconditioning in Markov chain Monte Carlo M Hird, S Livingstone arXiv preprint arXiv:2312.04898, 2023	3	2023
Some contributions to the theory and methodology of Markov chain Monte Carlo SJ Livingstone UCL (University College London), 2016	2	2016
Structure Learning with Adaptive Random Neighborhood Informed MCMC X Liang, A Caron, S Livingstone, J Griffin Advances in Neural Information Processing Systems 36, 2024	1*	2024
Averaging polyhazard models using Piecewise deterministic Monte Carlo with applications to data with long-term survivors L Hardcastle, S Livingstone, G Baio arXiv preprint arXiv:2406.14182, 2024		2024
Skew-symmetric schemes for stochastic differential equations with non-Lipschitz drift: an unadjusted Barker algorithm S Livingstone, N Nüsken, G Vasdekis, RY Zhang arXiv preprint arXiv:2405.14373, 2024		2024

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