A principled stopping rule for importance sampling

DOI10.1214/22-EJS2074OpenAlexW3196441013MaRDI QIDQ2106773

Víctor Elvira, Dootika Vats, Medha Agarwal

Publication date: 19 December 2022

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2108.13289

zbMATH Keywords

Monte Carlodiagnosticseffective sample size

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Monte Carlo methods (65C05)

Uses Software

• R-INLA

Cites Work

• Unnamed Item
• Tests for standardized generalized variances of multivariate normal populations of possibly different dimensions
• The asymptotic validity of sequential stopping rules for stochastic simulations
• Markov chain Monte Carlo with the integrated nested Laplace approximation
• The sample size required in importance sampling
• Importance sampling the union of rare events with an application to power systems analysis
• Product-form estimators: exploiting independence to scale up Monte Carlo
• Bayesian order-restricted inference of a Weibull multi-step step-stress model
• Generalized multiple importance sampling
• Layered adaptive importance sampling
• Adaptive Multiple Importance Sampling
• Importance Sampling and Necessary Sample Size: An Information Theory Approach
• Relative fixed-width stopping rules for Markov chain Monte Carlo simulations
• CERTAIN GENERALIZATIONS IN THE ANALYSIS OF VARIANCE
• Bayesian Inference in the Presence of Intractable Normalizing Functions
• Fixed-Width Sequential Stopping Rules for a Class of Stochastic Programs
• Multivariate output analysis for Markov chain Monte Carlo
• Importance Sampling with the Integrated Nested Laplace Approximation

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