Samuel Livingstone
Samuel Livingstone
Associate professor in mathematical statistics, University College London
Verified email at - Homepage
Cited by
Cited by
The geometric foundations of hamiltonian monte carlo
MJ Betancourt, S Byrne, S Livingstone, M Girolami
Bernoulli 23 (4A), 2257-2298, 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
On the geometric ergodicity of Hamiltonian Monte Carlo
S Livingstone, M Betancourt, S Byrne, M Girolami
Bernoulli 25 (4A), 3109-3138, 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
Kinetic energy choice in Hamiltonian/hybrid Monte Carlo
S Livingstone, MF Faulkner, GO Roberts
Biometrika 106 (2), 303-319, 2019
Information-geometric Markov chain Monte Carlo methods using diffusions
S Livingstone, M Girolami
Entropy 16 (6), 3074-3102, 2014
Peskun–Tierney ordering for Markovian Monte Carlo: beyond the reversible scenario
C Andrieu, S Livingstone
The Annals of Statistics 49 (4), 1958-1981, 2021
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
A general perspective on the Metropolis-Hastings kernel
C Andrieu, A Lee, S Livingstone
arXiv preprint arXiv:2012.14881, 2020
Geometric ergodicity of the Random Walk Metropolis with position-dependent proposal covariance
S Livingstone
Mathematics 9 (4), 341, 2021
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
Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms
J Vogrinc, S Livingstone, G Zanella
Biometrika, 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
Adaptive MCMC for Bayesian variable selection in generalised linear models and survival models
X Liang, S Livingstone, J Griffin
Entropy 25 (9), 1310, 2023
Sampling algorithms in statistical physics: a guide for statistics and machine learning
MF Faulkner, S Livingstone
Statistical Science, 2023
Quantifying the effectiveness of linear preconditioning in Markov chain Monte Carlo
M Hird, S Livingstone
arXiv preprint arXiv:2312.04898, 2023
Some contributions to the theory and methodology of Markov chain Monte Carlo
SJ Livingstone
UCL (University College London), 2016
A Bayesian hierarchical model for predicting rates of oxygen consumption in mechanically-ventilated Intensive Care patients
L Hardcastle, S Livingstone, C Black, F Ricciardi, G Baio
Statistical Modelling: An International Journal, 2024
Structure Learning with Adaptive Random Neighborhood Informed MCMC
X Liang, A Caron, S Livingstone, J Griffin
Advances in Neural Information Processing Systems 36, 2024
Modelling the association between weather and short-term demand for children’s intensive care transport services during winter in the South East of England
S Livingstone, C Pagel, Z Shao, E Randle, P Ramnarayan
Operations Research for Health Care 31, 100327, 2021
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