Information-Geometric Markov Chain Monte Carlo Methods Using Diffusions. - Samuel Livingstone; Mark A. Girolami (Entropy, 2014)

Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families. - Heiko Strathmann; Dino Sejdinovic; Samuel Livingstone; Zoltán Szabó 001; Arthur Gretton (NIPS, 2015)

A fresh Take on 'Barker Dynamics' for MCMC. - Max Hird; Samuel Livingstone; Giacomo Zanella (MCQMC, 2020)

Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable selection. - Xitong Liang; Samuel Livingstone; Jim E. Griffin (Stat. Comput., 2022)

Structure Learning with Adaptive Random Neighborhood Informed MCMC. - Xitong Liang; Alberto Caron; Samuel Livingstone; Jim E. Griffin (NeurIPS, 2023)

Adaptive MCMC for Bayesian Variable Selection in Generalised Linear Models and Survival Models. - Xitong Liang; Samuel Livingstone; Jim E. Griffin (Entropy, 2023)

Skew-symmetric schemes for stochastic differential equations with non-Lipschitz drift: an unadjusted Barker algorithm. - Samuel Livingstone; Nikolas Nüsken; Giorgos Vasdekis; Rui-Yang Zhang (CoRR, 2024)