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
Accepted author manuscript, 443 KB, PDF document
Available under license: CC BY-NC-ND: Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License
Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
| <mark>Journal publication date</mark> | 1/05/2018 |
|---|---|
| <mark>Journal</mark> | Statistics and Probability Letters |
| Volume | 136 |
| Number of pages | 7 |
| Pages (from-to) | 148-154 |
| Publication Status | Published |
| Early online date | 2/03/18 |
| <mark>Original language</mark> | English |
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.