{"paper":{"title":"Exploring Pareto smoothing in sequential Monte Carlo","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.CO","authors_text":"Jia Le Tan, Nicola D. Walker, Richard G. Everitt","submitted_at":"2026-06-24T15:53:06Z","abstract_excerpt":"A popular technique for reducing the variance of importance sampling (IS) estimators is to modify the weights of some importance points. One approach is to truncate the largest weights, which reduces variance but can introduce substantial bias. Pareto smoothed importance sampling (PSIS), by contrast, reduces the variance of the weights by fitting a generalised Pareto distribution to the upper tail of the weight distribution and replacing the weights in this tail with the corresponding expected quantiles from the fitted distribution. PSIS can therefore also reduce variance, but typically with l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.25983","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.25983/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}