Improving SAMC using smoothing methods: Theory and applications to Bayesian model selection problems

DOI10.1214/07-AOS577zbMath1182.62162arXiv0908.3553MaRDI QIDQ834357

Faming Liang

Publication date: 19 August 2009

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

Full work available at URL: https://arxiv.org/abs/0908.3553

zbMATH Keywords

smoothingMarkov chain Monte Carlostochastic approximation Monte Carloreversible jump

Mathematics Subject Classification ID

Computational methods in Markov chains (60J22) Bayesian inference (62F15) Monte Carlo methods (65C05) Numerical analysis or methods applied to Markov chains (65C40) Stochastic approximation (62L20)

Related Items

Stochastic approximation Hamiltonian Monte Carlo ⋮ Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants ⋮ Adaptive spectral estimation for nonstationary multivariate time series ⋮ Weak Convergence Rates of Population Versus Single-Chain Stochastic Approximation MCMC Algorithms ⋮ Trajectory averaging for stochastic approximation MCMC algorithms ⋮ Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities ⋮ Stochastic approximation Monte Carlo EM for change-point analysis ⋮ Simulated Stochastic Approximation Annealing for Global Optimization With a Square-Root Cooling Schedule ⋮ Adaptive Bayesian Time–Frequency Analysis of Multivariate Time Series

Uses Software

• R
• S-PLUS

Cites Work

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