Target Density Normalization for Markov Chain Monte Carlo Algorithms

2014/10/27 by A. Caldwell, Chang Liu, Caldwell, Allen +1
Mathematics · #Data Analysis #FOS: Physical sciences #High Energy Physics - Experiment (hep-ex) #Instrumentation and Methods for Astrophysics (astro-ph.IM) #Markov Chains and Monte Carlo Methods #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistics and Probability (physics.data-an)

paper · pdf · doi:10.48550/arxiv.1410.7149

openalex publication_date 2014/10/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Techniques for evaluating the normalization integral of the target density for Markov Chain Monte Carlo algorithms are described and tested numerically. It is assumed that the Markov Chain algorithm has converged to the target distribution and produced a set of samples from the density. These are used to evaluate sample mean, harmonic mean and Laplace algorithms for the calculation of the integral of the target density. A clear preference for the sample mean algorithm applied to a reduced support region is found, and guidelines are given for implementation.

Citations

• Markov Chain Monte Carlo in Practice: A Roundtable Discussion

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