Sam Livingstone
Sam Livingstone
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TitleCited byYear
The geometric foundations of hamiltonian monte carlo
M 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
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, 955-963, 2015
On the geometric ergodicity of Hamiltonian Monte Carlo
S Livingstone, M Betancourt, S Byrne, M Girolami
Bernoulli 25 (4A), 3109-3138, 2019
Information-geometric Markov chain Monte Carlo methods using diffusions
S Livingstone, M Girolami
Entropy 16 (6), 3074-3102, 2014
Kinetic energy choice in Hamiltonian/hybrid Monte Carlo
S Livingstone, MF Faulkner, GO Roberts
Biometrika 106 (2), 303-319, 2019
Geometric ergodicity of the Random Walk Metropolis with position-dependent proposal covariance
S Livingstone
arXiv preprint arXiv:1507.05780, 2015
Peskun-Tierney ordering for Markov chain and process Monte Carlo: beyond the reversible scenario
C Andrieu, S Livingstone
arXiv preprint arXiv:1906.06197, 2019
On the robustness of gradient-based MCMC algorithms
S Livingstone, G Zanella
arXiv preprint arXiv:1908.11812, 2019
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Articles 1–9