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    Rights statement: This is the author’s version of a work that was accepted for publication in Statistics and Probability Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Statistics and Probability Letters, 136, 2018 DOI: 10.1016/j.spl.2018.02.021

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Piecewise deterministic Markov processes for scalable Monte Carlo on restricted domains

Research output: Contribution to journalJournal article

Published
  • Joris Bierkens
  • Alexandre Bouchard-Côté
  • Arnaud Doucet
  • Andrew B. Duncan
  • Paul Fearnhead
  • Thibaut Lienart
  • Gareth Roberts
  • Sebastian J. Vollmer
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<mark>Journal publication date</mark>1/05/2018
<mark>Journal</mark>Statistics and Probability Letters
Volume136
Number of pages7
Pages (from-to)148-154
Publication statusPublished
Early online date2/03/18
Original languageEnglish

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. (C) 2018 Elsevier B.V. All rights reserved.

Bibliographic note

This is the author’s version of a work that was accepted for publication in Statistics and Probability Letters. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Statistics and Probability Letters, 136, 2018 DOI: 10.1016/j.spl.2018.02.021