Cores for piecewise-deterministic Markov processes used in Markov chain Monte Carlo

DOI10.1214/21-ECP430zbMath1487.60137arXiv1910.11429OpenAlexW3214262673MaRDI QIDQ2078224

Peter Holderrieth

Publication date: 28 February 2022

Published in: Electronic Communications in Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1910.11429

zbMATH Keywords

Markov semigroupMarkov chain Monte CarloFeller processpiecewise-deterministic Markov processcoresbouncy particle samplerrandomized Hamiltonian Monte Carlo

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Continuous-time Markov processes on general state spaces (60J25) Monte Carlo methods (65C05)

Related Items (1)

PDMP characterisation of event-chain Monte Carlo algorithms for particle systems

Cites Work

• The Zig-Zag Process and Super-Efficient Sampling for Bayesian Analysis of Big Data
• The Bouncy Particle Sampler: A Non-Reversible Rejection-Free Markov Chain Monte Carlo Method
• An introduction to MCMC for machine learning
• Piecewise deterministic Markov processes for continuous-time Monte Carlo
• Randomized Hamiltonian Monte Carlo
• Exponential ergodicity of the bouncy particle sampler
• The Stone-Weierstrass Theorem
• The Markov chain Monte Carlo revolution
• Geometric integrators and the Hamiltonian Monte Carlo method
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