2017/02/27 by Manon Michel, Alain Durmus, Michel, Manon +3 · 3 citations
Mathematics · Physics and Astronomy · #Computation (stat.CO) #FOS: Computer and information sciences #Markov Chains and Monte Carlo Methods #Theoretical and Computational Physics
paper · doi:10.48550/arxiv.1702.08397
openalex publication_date 2017/02/27 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28
Irreversible and rejection-free Monte Carlo methods, recently developed in Physics under the name Event-Chain and known in Statistics as Piecewise Deterministic Monte Carlo (PDMC), have proven to produce clear acceleration over standard Monte Carlo methods, thanks to the reduction of their random-walk behavior. However, while applying such schemes to standard statistical models, one generally needs to introduce an additional randomization for sake of correctness. We propose here a new class of Event-Chain Monte Carlo methods that reduces this extra-randomization to a bare minimum. We compare the efficiency of this new methodology to standard PDMC and Monte Carlo methods. Accelerations up to several magnitudes and reduced dimensional scalings are exhibited.