Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains

Joris Bierkens, Alexandre Bouchard-Côté, Arnaud Doucet, Andrew B. Duncan, Paul Fearnhead, Thibaut Lienart, Gareth Roberts, Sebastian J. Vollmer

Research output: Contribution to journalArticleScientificpeer-review

7 Citations (Scopus)
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Abstract

Piecewise Deterministic Monte Carlo algorithms enable simulation from a posterior distribution, whilst only needing to access a sub-sample of data at each iteration. We show how they can be implemented in settings where the parameters live on a restricted domain.

Original languageEnglish
Pages (from-to)148-154
Number of pages7
JournalStatistics and Probability Letters
Volume136
DOIs
Publication statusPublished - 2018

Keywords

  • Bayesian statistics
  • Logistic regression
  • MCMC
  • Piecewise deterministic Markov processes

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    Bierkens, J., Bouchard-Côté, A., Doucet, A., Duncan, A. B., Fearnhead, P., Lienart, T., Roberts, G., & Vollmer, S. J. (2018). Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains. Statistics and Probability Letters, 136, 148-154. https://doi.org/10.1016/j.spl.2018.02.021