Importance sampling for families of distributions

DOI10.1214/aoap/1029962870zbMath0966.60061OpenAlexW2044181424MaRDI QIDQ1578598

Mauro Piccioni, Neal Madras

Publication date: 4 September 2000

Published in: The Annals of Applied Probability (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aoap/1029962870

zbMATH Keywords

importance samplingMarkov chain Monte Carlostatistical physics

Mathematics Subject Classification ID

Discrete-time Markov processes on general state spaces (60J05) Probabilistic methods, stochastic differential equations (65C99)

Related Items

Markov chain decomposition for convergence rate analysis, Efficient high-dimensional importance sampling, Selection of importance weights for monte carlo estimation of normalizing constants, Equi-energy sampling does not converge rapidly on the mean-field Potts model with three colors close to the critical temperature, The sample size required in importance sampling, Stochastic adaptive selection of weights in the simulated tempering algorithm, Opinion formation in a heterogeneous population --- a new approach to the Hopfield model, Conditions for rapid mixing of parallel and simulated tempering on multimodal distributions, The simulated tempering method in the infinite switch limit with adaptive weight learning, On swapping and simulated tempering algorithms., Monte Carlo study of the interacting self-avoiding walk model in three dimensions., Mixing times for the Swapping Algorithm on the Blume-Emery-Griffiths model

Uses Software

• tsbridge

Cites Work

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• Markov chains and stochastic stability
• Geometric bounds for eigenvalues of Markov chains
• Central limit theorem for additive functionals of reversible Markov processes and applications to simple exclusions
• Decay of correlations in classical lattice models at high temperature
• Monte Carlo study of the \(\Theta\)-point for collapsing trees
• A note on Metropolis-Hastings kernels for general state spaces
• Methods for approximating integrals in statistics with special emphasis on Bayesian integration problems
• Markov chain decomposition for convergence rate analysis
• Markov chains for exploring posterior distributions. (With discussion)
• Do stochastic algorithms avoid traps?
• Nonlocal Monte Carlo algorithm for self-avoiding walks with fixed endpoints.
• Logarithmic Sobolev inequalities for finite Markov chains
• Optimum Monte-Carlo sampling using Markov chains
• Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes
• Annealing Markov Chain Monte Carlo with Applications to Ancestral Inference
• Monte Carlo sampling methods using Markov chains and their applications
• Tools for statistical inference. Methods for the exploration of posterior distributions and likelihood functions.
• Monte Carlo simulation in statistical physics. An introduction.