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Samuel Livingstone
Samuel Livingstone
Lecturer in Mathematical Statistics, University College London
Verified email at ucl.ac.uk - Homepage
Title
Cited by
Cited by
Year
The geometric foundations of hamiltonian monte carlo
MJ Betancourt, S Byrne, S Livingstone, M Girolami
Bernoulli 23 (4A), 2257-2298, 2017
1752017
On the geometric ergodicity of Hamiltonian Monte Carlo
S Livingstone, M Betancourt, S Byrne, M Girolami
Bernoulli 25 (4A), 3109-3138, 2019
1132019
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
1122014
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
822015
Kinetic energy choice in Hamiltonian/hybrid Monte Carlo
S Livingstone, MF Faulkner, GO Roberts
Biometrika 106 (2), 303-319, 2019
502019
Information-geometric Markov chain Monte Carlo methods using diffusions
S Livingstone, M Girolami
Entropy 16 (6), 3074-3102, 2014
432014
Peskun–tierney ordering for markovian monte carlo: Beyond the reversible scenario
C Andrieu, S Livingstone
The Annals of Statistics 49 (4), 1958-1981, 2021
33*2021
The Barker proposal: combining robustness and efficiency in gradient-based MCMC
S Livingstone, G Zanella
Journal of the Royal Statistical Society: Series B, 2021
25*2021
Geometric ergodicity of the Random Walk Metropolis with position-dependent proposal covariance
S Livingstone
Mathematics 9 (4), 341, 2021
10*2021
A general perspective on the Metropolis-Hastings kernel
C Andrieu, A Lee, S Livingstone
arXiv preprint arXiv:2012.14881, 2020
102020
Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms
J Vogrinc, S Livingstone, G Zanella
arXiv preprint arXiv:2201.01123, 2022
32022
A fresh take on 'Barker dynamics' for MCMC
M Hird, S Livingstone, G Zanella
arXiv preprint arXiv:2012.09731, 2020
32020
Some contributions to the theory and methodology of Markov chain Monte Carlo
SJ Livingstone
UCL (University College London), 2016
22016
Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable Selection
X Liang, S Livingstone, J Griffin
arXiv preprint arXiv:2110.11747, 2021
12021
Sampling algorithms in statistical physics: a guide for statistics and machine learning
MF Faulkner, S Livingstone
arXiv preprint arXiv:2208.04751, 2022
2022
A Bayesian hierarchical model for improving exercise rehabilitation in mechanically ventilated ICU patients
L Hardcastle, S Livingstone, C Black, F Ricciardi, G Baio
arXiv preprint arXiv:2206.14047, 2022
2022
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
2021
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