Hypocoercivity of piecewise deterministic Markov process-Monte Carlo

DOI10.1214/20-AAP1653zbMath1476.60124arXiv1808.08592OpenAlexW3135584575MaRDI QIDQ2240892

Julien Roussel, Christophe Andrieu, Nikolas Nüsken, Alain Durmus

Publication date: 4 November 2021

Published in: The Annals of Applied Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1808.08592

zbMATH Keywords

geometric convergencehypoellipticityPDMCMC

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Continuous-time Markov processes on general state spaces (60J25) Numerical analysis or methods applied to Markov chains (65C40)

Related Items

On explicit \(L^2\)-convergence rate estimate for piecewise deterministic Markov processes in MCMC algorithms ⋮ Hypocoercivity with Schur complements ⋮ Complexity of zigzag sampling algorithm for strongly log-concave distributions ⋮ Spectral analysis of the zigzag process ⋮ Adaptive schemes for piecewise deterministic Monte Carlo algorithms ⋮ Exact targeting of Gibbs distributions using velocity-jump processes ⋮ Infinite dimensional piecewise deterministic Markov processes ⋮ On explicit \(L^2\)-convergence rate estimate for underdamped Langevin dynamics ⋮ Exponential ergodicity for damping Hamiltonian dynamics with state-dependent and non-local collisions ⋮ Polynomial convergence rates of piecewise deterministic Markov processes ⋮ Peskun-Tierney ordering for Markovian Monte Carlo: beyond the reversible scenario ⋮ A piecewise deterministic Monte Carlo method for diffusion bridges ⋮ Randomized Hamiltonian Monte Carlo as scaling limit of the bouncy particle sampler and dimension-free convergence rates ⋮ Ergodicity of the zigzag process ⋮ High-dimensional scaling limits of piecewise deterministic sampling algorithms ⋮ Weighted L ²-contractivity of Langevin dynamics with singular potentials ⋮ A note on the polynomial ergodicity of the one-dimensional Zig-Zag process

Uses Software

• BayesDA

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