A Theory for Dynamic Weighting in Monte Carlo Computation

DOI10.1198/016214501753168253zbMath1028.65003OpenAlexW2017691540MaRDI QIDQ4419444

Jun S. Liu, Faming Liang, Wing-Hung Wong

Publication date: 13 August 2003

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1198/016214501753168253

zbMATH Keywords

neural networkimportance samplingrenewal theorysimulated annealingMonte Carlo methodMarkov chainIsing modelMetropolis algorithmGibbs samplingsimulated temperingdynamic weighting algorithm

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Monte Carlo methods (65C05) Applications of Markov chains and discrete-time Markov processes on general state spaces (social mobility, learning theory, industrial processes, etc.) (60J20) Numerical analysis or methods applied to Markov chains (65C40) Markov renewal processes, semi-Markov processes (60K15) Dynamic lattice systems (kinetic Ising, etc.) and systems on graphs in time-dependent statistical mechanics (82C20)

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