フォロー
Samuel Livingstone
Samuel Livingstone
Associate professor in mathematical statistics, University College London
確認したメール アドレス: ucl.ac.uk - ホームページ
タイトル
引用先
引用先
年
The geometric foundations of hamiltonian monte carlo
MJ Betancourt, S Byrne, S Livingstone, M Girolami
Bernoulli 23 (4A), 2257-2298, 2017
2442017
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
1722014
On the geometric ergodicity of Hamiltonian Monte Carlo
S Livingstone, M Betancourt, S Byrne, M Girolami
Bernoulli 25 (4A), 3109-3138, 2019
1632019
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
942015
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
69*2022
Kinetic energy choice in Hamiltonian/hybrid Monte Carlo
S Livingstone, MF Faulkner, GO Roberts
Biometrika 106 (2), 303-319, 2019
642019
Peskun–Tierney ordering for Markovian Monte Carlo: beyond the reversible scenario
C Andrieu, S Livingstone
The Annals of Statistics 49 (4), 1958-1981, 2021
57*2021
Information-geometric Markov chain Monte Carlo methods using diffusions
S Livingstone, M Girolami
Entropy 16 (6), 3074-3102, 2014
522014
A general perspective on the Metropolis-Hastings kernel
C Andrieu, A Lee, S Livingstone
arXiv preprint arXiv:2012.14881, 2020
282020
Geometric ergodicity of the Random Walk Metropolis with position-dependent proposal covariance
S Livingstone
Mathematics 9 (4), 341, 2021
16*2021
Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms
J Vogrinc, S Livingstone, G Zanella
Biometrika, 2022
152022
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
122022
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
92022
Sampling algorithms in statistical physics: a guide for statistics and machine learning
MF Faulkner, S Livingstone
Statistical Science, 2023
82023
Adaptive mcmc for bayesian variable selection in generalised linear models and survival models
X Liang, S Livingstone, J Griffin
Entropy 25 (9), 1310, 2023
72023
Quantifying the effectiveness of linear preconditioning in Markov chain Monte Carlo
M Hird, S Livingstone
arXiv preprint arXiv:2312.04898, 2023
52023
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
22024
Some contributions to the theory and methodology of Markov chain Monte Carlo
SJ Livingstone
UCL (University College London), 2016
22016
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
現在システムで処理を実行できません。しばらくしてからもう一度お試しください。
論文 1–20