Adaptive importance sampling in monte carlo integration

DOI10.1080/00949659208810398zbMath0781.65016OpenAlexW2022637125MaRDI QIDQ5287303

Man-Suk Oh, James O. Berger

Publication date: 17 February 1994

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949659208810398

zbMATH Keywords

convergenceMonte Carlo methoditerative methodquadrature formulavariance reductionMonte Carlo integrationstopping rulesadaptive importance sampling

Mathematics Subject Classification ID

Monte Carlo methods (65C05) Approximate quadratures (41A55) Numerical quadrature and cubature formulas (65D32)

Related Items (23)

Sequential ensemble transform for Bayesian inverse problems ⋮ Adaptive radial-based direction sampling: some flexible and robust Monte Carlo integration methods ⋮ Selection of importance weights for monte carlo estimation of normalizing constants ⋮ A computational procedure for estimation of the mixing time of the random-scan Metropolis algorithm ⋮ Approximate verification of geometric ergodicity for multiple-step Metropolis transition kernels ⋮ Unnamed Item ⋮ SISAM and MIXIN: Two algorithms for the computation of posterior moments and densities using Monte Carlo integration ⋮ Posterior exploration based sequential Monte Carlo for global optimization ⋮ Iterative Bayesian inversion with Gaussian mixtures: finite sample implementation and large sample asymptotics ⋮ Adaptive multiple importance sampling for Gaussian processes ⋮ Batching Adaptive Variance Reduction ⋮ Improved training of physics-informed neural networks for parabolic differential equations with sharply perturbed initial conditions ⋮ Iterative Importance Sampling Algorithms for Parameter Estimation ⋮ A path sampling identity for computing the Kullback-Leibler and J divergences ⋮ Geometric ergodicity of a Metropolis-Hastings algorithm for Bayesian inference of phylogenetic branch lengths ⋮ A Monte Carlo integration approach to estimating drift and minorization coefficients for Metropolis-Hastings samplers ⋮ Adaptive importance sampling Monte Carlo simulation for general multivariate probability laws ⋮ Integral approximation by kernel smoothing ⋮ Controlled stratification for quantile estimation ⋮ Safe adaptive importance sampling: a mixture approach ⋮ Estimating the Effect of Parameter Uncertainty in Repeated Sample Surveys ⋮ Infinite-dimensional gradient-based descent for alpha-divergence minimisation ⋮ Sequential Monte Carlo samplers with independent Markov chain Monte Carlo proposals

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Statistical decision theory and Bayesian analysis. 2nd ed
• Posterior moments computed by mixed integration
• A 1-1 poly-t random variable generator with application to Monte Carlo integration
• Antithetic acceleration of Monte Carlo integration in Bayesian inference
• Further experience in Bayesian analysis using Monte Carlo integration
• Applications of a Method for the Efficient Computation of Posterior Distributions
• The implementation of the bayesian paradigm
• Accurate Approximations for Posterior Moments and Marginal Densities
• Latent class analysis of two-way contingency tables by Bayesian methods
• Multiparameter Univariate Bayesian Analysis
• Bayesian Estimates of Equation System Parameters: An Application of Integration by Monte Carlo
• Bayesian Inference in Econometric Models Using Monte Carlo Integration

This page was built for publication: Adaptive importance sampling in monte carlo integration