On the use of stochastic approximation Monte Carlo for Monte Carlo integration

DOI10.1016/j.spl.2008.10.007zbMath1156.62344OpenAlexW2028323834MaRDI QIDQ1007341

Faming Liang

Publication date: 20 March 2009

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.spl.2008.10.007

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Stochastic approximation (62L20) Numerical integration (65D30)

Related Items

An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization ⋮ Bayesian Subset Modeling for High-Dimensional Generalized Linear Models ⋮ Exact inference in contingency tables via stochastic approximation Monte Carlo ⋮ Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities ⋮ Stochastic approximation Monte Carlo EM for change-point analysis ⋮ Learning Bayesian networks for discrete data ⋮ Longitudinal functional principal component modelling via Stochastic Approximation Monte Carlo ⋮ Simulated Stochastic Approximation Annealing for Global Optimization With a Square-Root Cooling Schedule ⋮ Double-parallel Monte Carlo for Bayesian analysis of big data

Cites Work

• Unnamed Item
• Equi-energy sampler with applications in statistical inference and statistical mechanics
• Markov chains and stochastic stability
• General state space Markov chains and MCMC algorithms
• An extension of Shannon-McMillan theorem and some limit properties for nonhomogeneous Markov chains
• Annealing stochastic approximation Monte Carlo algorithm for neural network training
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Geometric convergence and central limit theorems for multidimensional Hastings and Metropolis algorithms
• Dynamic weighting in Monte Carlo and optimization
• Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models
• Bayesian Inference in Econometric Models Using Monte Carlo Integration
• Minorization Conditions and Convergence Rates for Markov Chain Monte Carlo
• Equation of State Calculations by Fast Computing Machines
• Stochastic Approximation in Monte Carlo Computation
• Stability of Stochastic Approximation under Verifiable Conditions
• Monte Carlo sampling methods using Markov chains and their applications
• A Stochastic Approximation Method
• An adaptive Metropolis algorithm