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 language | English |
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Pages (from-to) | 148-154 |
Number of pages | 7 |
Journal | Statistics and Probability Letters |
Volume | 136 |
DOIs | |
Publication status | Published - 2018 |
Bibliographical note
Accepted author manuscriptKeywords
- Bayesian statistics
- Logistic regression
- MCMC
- Piecewise deterministic Markov processes