Markov chain Monte Carlo sampling using a reservoir method

DOI10.1016/j.csda.2019.05.001OpenAlexW2943879660MaRDI QIDQ2002718

Zhonglei Wang

Publication date: 12 July 2019

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2019.05.001

zbMATH Keywords

convergenceMetropolis-Hastings algorithmremoval procedurestochastic thinning

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Monte Carlo methods (65C05)

Uses Software

• BayesDA

Cites Work

• Unnamed Item
• Weighted random sampling with a reservoir
• Slice sampling. (With discussions and rejoinder)
• Inference from iterative simulation using multiple sequences
• Markov chains for exploring posterior distributions. (With discussion)
• Sampling-Based Approaches to Calculating Marginal Densities
• Random sampling with a reservoir
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Faster methods for random sampling
• Adaptive Rejection Metropolis Sampling within Gibbs Sampling
• The Multiple-Try Method and Local Optimization in Metropolis Sampling
• Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods
• Equation of State Calculations by Fast Computing Machines
• Measure Theory and Probability Theory
• Monte Carlo sampling methods using Markov chains and their applications

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