On the empirical efficiency of local MCMC algorithms with pools of proposals

DOI10.1002/CJS.11196zbMath1281.65002OpenAlexW1589822290MaRDI QIDQ2870715

Matei Mireuta, Mylène Bédard

Publication date: 21 January 2014

Published in: Canadian Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/cjs.11196

zbMATH Keywords

numerical examplesregressionlinear regressionMetropolis algorithmMarkov chain Monte Carlo methodscomputational costcorrelated proposalsdelayed rejection strategyBayesian logistic regressionsmultiple-try algorithm

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) General nonlinear regression (62J02) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (2)

Equation-solving estimator based on the general n-step MHDR algorithm ⋮ Conditional sequential Monte Carlo in high dimensions

Cites Work

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