Piecewise Deterministic Markov Processes for Scalable Monte Carlo on Restricted Domains
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Abstract (arXiv)
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.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Jan 2017.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1; 2025: 1.
4 papers.
- 2024Piecewise deterministic generative modelscited by 4
- 2022Efficient computation of the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processescited by 3
- 2021The Application of Zig-Zag Sampler in Sequential Markov Chain Monte Carlocited by 0
- 2025Numerical Generalized Randomized Hamiltonian Monte Carlo for piecewise smooth target densitiescited by 0
- 2025Numerical Generalized Randomized Hamiltonian Monte Carlo for piecewise smooth target densities25 Apr 2025
- 2024Piecewise deterministic generative models28 Jul 2024
- 2022Efficient computation of the volume of a polytope in high-dimensions using Piecewise Deterministic Markov Processes18 Feb 2022
- 2021The Application of Zig-Zag Sampler in Sequential Markov Chain Monte Carlo18 Nov 2021