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Introduction to Markov Chain Monte Carlo Simulations and their Statistical Analysis

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arxiv cond-mat/0410490 v1 pith:KOR2462Y submitted 2004-10-19 cond-mat.stat-mech cond-mat.mtrl-scihep-latphysics.bio-phphysics.chem-phphysics.comp-phphysics.ed-ph

classification cond-mat.stat-mechcond-mat.mtrl-scihep-latphysics.bio-phphysics.chem-phphysics.comp-phphysics.ed-ph
keywords analysiscarlochainmarkovmontesimulationsstatisticalarticle
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This article is a tutorial on Markov chain Monte Carlo simulations and their statistical analysis. The theoretical concepts are illustrated through many numerical assignments from the author's book on the subject. Computer code (in Fortran) is available for all subjects covered and can be downloaded from the web.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DLScanner: A parameter space scanner package assisted by deep learning methods

    hep-ph 2024-12 conditional novelty 6.0 of 10

    A new scanner package combines a similarity-learning neural network with VEGAS adaptive sampling to collect valid points in BSM parameter scans faster than earlier ML-based methods.

  2. Mapping parametric error profiles onto nuclear structure configurations in deformed proton radioactivity

    nucl-th 2026-07 reject novelty 5.0 of 10

    Bayesian fits of a deformed Woods–Saxon WKB model to fourteen proton-decay half-lives show widening parameter-uncertainty bands near Z=82, which the paper reads as a signature of potential softening.

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