2008/07/28 by Y. Ascasíbar, Yago Ascasibar
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Artificial intelligence #Bayesian Methods and Mixture Models #Bayesian probability #Computer science #Context (archaeology) #Estimator #Field (mathematics) #Gaussian Processes and Bayesian Inference #Hybrid Monte Carlo #Importance sampling #Markov chain Monte Carlo #Mathematics #Monte Carlo algorithm #Monte Carlo integration #Monte Carlo method #Monte Carlo method in statistical physics #Numerical integration #Physics #Quasi-Monte Carlo method #Sampling (signal processing) #Scientific Research and Discoveries #Statistical physics #Statistics #astro-ph #cs.DS
paper · pdf · doi:10.1016/j.cpc.2008.07.011
18 pages, 3 figures, submitted to Comp. Phys. Comm
arxiv created 2008/07/28 · openalex publication_date 2008/08/06 · arxiv updated 2009/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
This paper describes a new algorithm for Monte Carlo integration, based on the Field Estimator for Arbitrary Spaces (FiEstAS). The algorithm is discussed in detail, and its performance is evaluated in the context of Bayesian analysis, with emphasis on multimodal distributions with strong parameter degeneracies. Source code is available upon request.