General hit-and-run Monte Carlo sampling for evaluating multidimensional integrals

DOI10.1016/0167-6377(96)00030-2zbMath0912.65011OpenAlexW1996681199MaRDI QIDQ1360109

Ming-Hui Chen, Bruce W. Schmeiser

Publication date: 18 May 1999

Published in: Operations Research Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-6377(96)00030-2

zbMATH Keywords

convergenceMarkov chainMonte Carlo samplingBayesian posterior distributionshigh-dimensional integralunbounded integrandsunbounded regions

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Numerical quadrature and cubature formulas (65D32)

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Cites Work

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• General hit-and-run Monte Carlo sampling for evaluating multidimensional integrals
• Markov chains for exploring posterior distributions. (With discussion)
• Direction Choice for Accelerated Convergence in Hit-and-Run Sampling
• Optimum Monte-Carlo sampling using Markov chains
• General Irreducible Markov Chains and Non-Negative Operators
• Sampling-Based Approaches to Calculating Marginal Densities
• Applications of a Method for the Efficient Computation of Posterior Distributions
• Accurate Approximations for Posterior Moments and Marginal Densities
• The Calculation of Posterior Distributions by Data Augmentation
• Bayesian Inference in Econometric Models Using Monte Carlo Integration
• Equation of State Calculations by Fast Computing Machines
• Hit-and-Run Algorithms for Generating Multivariate Distributions
• Monte Carlo sampling methods using Markov chains and their applications