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Samuel Livingstone
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Journal Articles
- 2023
- [j3]Xitong Liang, Samuel Livingstone, Jim E. Griffin:
Adaptive MCMC for Bayesian Variable Selection in Generalised Linear Models and Survival Models. Entropy 25(9): 1310 (2023) - 2022
- [j2]Xitong Liang, Samuel Livingstone, Jim E. Griffin:
Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable selection. Stat. Comput. 32(5): 84 (2022) - 2014
- [j1]Samuel Livingstone, Mark A. Girolami:
Information-Geometric Markov Chain Monte Carlo Methods Using Diffusions. Entropy 16(6): 3074-3102 (2014)
Conference and Workshop Papers
- 2023
- [c3]Xitong Liang, Alberto Caron, Samuel Livingstone, Jim E. Griffin:
Structure Learning with Adaptive Random Neighborhood Informed MCMC. NeurIPS 2023 - 2020
- [c2]Max Hird, Samuel Livingstone, Giacomo Zanella:
A fresh Take on 'Barker Dynamics' for MCMC. MCQMC 2020: 169-184 - 2015
- [c1]Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone, Zoltán Szabó, Arthur Gretton:
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. NIPS 2015: 955-963
Informal and Other Publications
- 2024
- [i2]Samuel Livingstone, Nikolas Nüsken, Giorgos Vasdekis, Rui-Yang Zhang:
Skew-symmetric schemes for stochastic differential equations with non-Lipschitz drift: an unadjusted Barker algorithm. CoRR abs/2405.14373 (2024) - 2023
- [i1]Alberto Caron, Xitong Liang, Samuel Livingstone, Jim E. Griffin:
Structure Learning with Adaptive Random Neighborhood Informed MCMC. CoRR abs/2311.00599 (2023)
Coauthor Index
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