For a full publications list see my Google scholar profile.  I'm currently highlighting:

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Preprints​

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• M. Hird & S. Livingstone. Quantifying the effectiveness of linear preconditioning in Markov chain Monte Carlo. [arXiv]

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• C. Andrieu, A. Lee & S. Livingstone. A general perspective on the Metropolis--Hastings kernel. [arXiv]

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Peer-reviewed

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• L. Hardcastle, S. Livingstone, C. Black, F Ricciardi & G. Baio​. A Bayesian hierarchical model for improving exercise rehabilitation in mechanically ventilated ICU patients. Statistical modelling: an international journal (forthcoming). [arXiv]

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• A. Caron, X. Liang, S. Livingstone & J. Griffin. Structure learning with adaptive random neighourhood informed MCMC. NeurIPS2023 (forthcoming). [arXiv]

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• M. Faulkner & S. Livingstone. Sampling algorithms in statistical physics: a guide for statistics and machine learning. Statistical Science (forthcoming). [arXiv]

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• J. Vogrinc, S. Livingstone & G. Zanella. Optimal design of the Barker proposal and other locally-balanced Metropolis--Hastings algorithms. Biometrika (forthcoming). [arXiv]

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• X. Liang, S. Livingstone & J. Griffin (2022). Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable Selection. Statistics and Computing. [link]

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• S. Livingstone & G. Zanella (2022). The Barker Proposal: combining robustness and efficiency in gradient-based MCMC. Journal of the Royal Statistical Society: Series B [link][10m_video][25m_video]

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• M. Hird, S. Livingstone & G. Zanella (2022). A fresh take on 'Barker dynamics' for MCMC. Proceedings of MCQMC2020 [link][arXiv]

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• C. Andrieu & S. Livingstone (2021). Peskun--Tierney orderings for Markovian Monte Carlo: beyond the reversible scenario. Annals of Statistics. [link]

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• S. Livingstone, C. Pagel, Z. Shao, E. Randle & P. Ramnarayan (2021). Modelling the association between weather and short term demand for children's intensive care transport services during winter in the South East of England. Operations Research for Healthcare. [link]

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• S. Livingstone (2021). Geometric ergodicity of the Random Walk Metropolis with position-dependent proposal covariance. Mathematics.  [link]

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• S. Livingstone, M. Faulkner & G. Roberts (2019). Kinetic energy choice in Hamiltonian/hybrid Monte Carlo. Biometrika. [link][45m_video]

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• S. Livingstone, M. Betancourt, S. Byrne & M. Girolami (2019). On the geometric ergodicity of Hamiltonian Monte Carlo. Bernoulli. [link]

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• M. Betancourt, S. Byrne, S. Livingstone & M. Girolami (2017). The geometric foundations of Hamiltonian Monte Carlo. Bernoulli. [link]

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• H. Strathmann, D. Sejdinovic, S. Livingstone, Z. Szabo & A. Gretton (2015). Gradient-free Hamiltonian Monte Carlo with efficient kernel exponential families. NeurIPS. [link]

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• T. Xifara, C. Sherlock, S. Livingstone, S. Byrne & M. Girolami (2014). Langevin diffusions and the Metropolis-adjusted Langevin algorithm. Statistics & Probability Letters. [link]

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• S. Livingstone & M. Girolami (2014). Information-geometric Markov chain Monte Carlo methods using diffusions. Entropy. [link]

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