2014/04/30 by Kevin Kröninger, Kevin Kroeninger, S. Schumann +2 · 25 citations
Computer Science · Economics, Econometrics and Finance · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Bayesian Methods and Mixture Models #Computer science #Computer vision #Financial Risk and Volatility Modeling #Markov chain Monte Carlo #Mathematics #Monte Carlo method #Phase space #Physics #Sampling (signal processing) #Statistical Distribution Estimation and Applications #Statistical physics #Statistics #Thermodynamics #hep-ph #physics.data-an
paper · pdf · doi:10.1016/j.cpc.2014.08.024
published in Computer Physics Communications 186, 1-10 (Elsevier BV) · version accepted for publication in CPC, largely extended examples section
openalex publication_date 2014/09/28 · arxiv created 2014/10/08 · arxiv updated 2015/06/12 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
A new Monte Carlo algorithm for phase-space sampling, named (MC)**3, is presented. It is based on Markov Chain Monte Carlo techniques but at the same time incorporates prior knowledge about the target distribution in the form of suitable phase-space mappings from a corresponding Multi-Channel importance sampling Monte Carlo. The combined approach inherits the benefits of both techniques while typical drawbacks of either solution get ameliorated.