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A fresh take on 'Barker dynamics' for MCMC

Hird, M; Livingstone, S; Zanella, G; (2022) A fresh take on 'Barker dynamics' for MCMC. In: Monte Carlo and Quasi-Monte Carlo Methods. (pp. pp. 169-184). Springer Nature: Cham, Switzerland. Green open access

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Abstract

We study a recently introduced gradient-based Markov chain Monte Carlo method based on ‘Barker dynamics’. We provide a full derivation of the method from first principles, placing it within a wider class of continuous-time Markov jump processes. We then evaluate the Barker approach numerically on a challenging ill-conditioned logistic regression example with imbalanced data, showing in particular that the algorithm is remarkably robust to irregularity (in this case a high degree of skew) in the target distribution.

Type: Proceedings paper
Title: A fresh take on 'Barker dynamics' for MCMC
Event: International Conference on Monte Carlo and Quasi-Monte Carlo Methods in Scientific Computing 2020
ISBN-13: 978-3-030-98318-5
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-98319-2_8
Publisher version: https://doi.org/10.1007/978-3-030-98319-2_8
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.
Keywords: MCMC, Markov chains, Barker dynamics, Langevin dynamics, Metropolis–Hastings
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science
URI: https://discovery.ucl.ac.uk/id/eprint/10136479
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