How reproducible is "Pareto Smoothed Importance Sampling"?

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Pareto Smoothed Importance Sampling

Aki Vehtari, Andrew Gelman, Jonah Gabry, Yao, Yuling, Gabry, Jonah

Published: Jul 9, 2015

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Evidence: Inferred

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DOI Publisher

Importance weighting is a general way to adjust Monte Carlo integration to account for draws from the wrong distribution, but the resulting estimate can be highly variable when the importance ratios have a heavy right tail. This routinely occurs when there are aspects of the target distribution that are not well captured by the approximating distribution, in which case more stable estimates can be obtained by modifyi ...

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ng extreme importance ratios. We present a new method for stabilizing importance weights using a generalized Pareto distribution fit to the upper tail of the distribution of the simulated importance ratios. The method, which empirically performs better than existing methods for stabilizing importance sampling estimates, includes stabilized effective sample size estimates, Monte Carlo error estimates, and convergence diagnostics. The presented Pareto $\hat{k}$ finite sample convergence rate diagnostic is useful for any Monte Carlo estimator.

Technical details

Canonical key: doi-10.48550_arxiv.1507.02646

Cache status: Stale (SWR served)

Generated at: Jul 23, 2026, 4:32 PM

Artifact coverage: sparse

HF provider: ok (mixed)

PWC source used: No

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HF policy: hf-relevance-v27

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Time to repro: a few hours

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Results & Benchmarks

Freshness tier: cold

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Implementation Evidence Summary

Confidence: low

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Reproduction Risks

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Evidence disclosure

Evidence graph: 2 refs, 1 links.

Utility signals: depth 60/100, grounding 58/100, status medium.

Implementation Status

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Last checked: Jul 23, 2026

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Research context

229

Citations

References

Tasks

Monte Carlo method, Estimator, Pareto principle, Weighting, Convergence (economics), Pareto distribution, Rate of convergence, Pareto interpolation

Methods

Mathematical optimization

Domains

Distribution (mathematics), Mathematics

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