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.
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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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