Importance Sampling and Necessary Sample Size: An Information Theory Approach

DOI10.1137/16M1093549zbMath1396.65016arXiv1608.08814OpenAlexW2963371806WikidataQ129694106 ScholiaQ129694106MaRDI QIDQ3176248

Daniel Sanz-Alonso

Publication date: 19 July 2018

Published in: SIAM/ASA Journal on Uncertainty Quantification (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1608.08814

zbMATH Keywords

sample sizeimportance samplinginformation theory\(f\)-divergence

Mathematics Subject Classification ID

Sampling theory, sample surveys (62D05) Statistical aspects of information-theoretic topics (62B10)

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Unnamed Item ⋮ Autodifferentiable Ensemble Kalman Filters ⋮ Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis ⋮ Overcoming the timescale barrier in molecular dynamics: Transfer operators, variational principles and machine learning ⋮ A unified performance analysis of likelihood-informed subspace methods ⋮ Context-Aware Surrogate Modeling for Balancing Approximation and Sampling Costs in Multifidelity Importance Sampling and Bayesian Inverse Problems ⋮ Kernel Methods for Bayesian Elliptic Inverse Problems on Manifolds ⋮ A Practical Example for the Non-linear Bayesian Filtering of Model Parameters ⋮ Importance sampling: intrinsic dimension and computational cost ⋮ Convergence rates for optimised adaptive importance samplers ⋮ A principled stopping rule for importance sampling

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• On Choosing and Bounding Probability Metrics
• Methods of Reducing Sample Size in Monte Carlo Computations
• On Information and Sufficiency
• Information Theory and Statistics: A Tutorial

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