Componentwise adaptation for high dimensional MCMC

DOI10.1007/BF02789703zbMath1091.65009OpenAlexW2028147833WikidataQ59866673 ScholiaQ59866673MaRDI QIDQ2488398

Eero Saksman, Heikki Haario, Johanna Tamminen

Publication date: 24 May 2006

Published in: Computational Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf02789703

zbMATH Keywords

numerical examplesprincipal componentsmultivariate distributionMarkov chain Monte Carlo (MCMC) methodrandom vectors generatingsingle component adaptive Metropolis sampling algorithm

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Factor analysis and principal components; correspondence analysis (62H25) Sampling theory, sample surveys (62D05) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)

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Cites Work

• Adaptive proposal distribution for random walk Metropolis algorithm
• On adaptive Markov chain Monte Carlo algorithms
• Self-regenerative Markov chain Monte Carlo with adaptation
• Componentwise adaptation for high dimensional MCMC
• Adaptive Markov Chain Monte Carlo through Regeneration
• Equation of State Calculations by Fast Computing Machines
• Monte Carlo sampling methods using Markov chains and their applications
• An adaptive Metropolis algorithm

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