{"paper":{"title":"GetDist: a Python package for analysing Monte Carlo samples","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"GetDist provides automated kernel density estimation for weighted and correlated Monte Carlo samples with boundary corrections.","cross_cats":["astro-ph.CO","physics.data-an"],"primary_cat":"astro-ph.IM","authors_text":"Antony Lewis","submitted_at":"2019-10-30T16:38:10Z","abstract_excerpt":"Monte Carlo techniques, including MCMC and other methods, are widely used in Bayesian inference to generate sets of samples from a parameter space of interest. The Python GetDist package provides tools for analysing these samples and calculating marginalized one- and two-dimensional densities using Kernel Density Estimation (KDE). Many Monte Carlo methods produce correlated and/or weighted samples, for example produced by MCMC, nested, or importance sampling, and there can be hard boundary priors. GetDist's baseline method consists of applying a linear boundary kernel, and then using multiplic"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"GetDist's baseline method consists of applying a linear boundary kernel, and then using multiplicative bias correction. The smoothing bandwidth is selected automatically following Botev et al., based on a mixture of heuristics and optimization results using the expected scaling with an effective number of samples.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the automatic bandwidth selection and linear boundary kernel produce accurate densities for the typical distributions and boundary conditions encountered in cosmological and physical parameter inference without requiring user tuning.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"GetDist implements boundary-corrected KDE with automatic smoothing for analyzing weighted and correlated Monte Carlo samples, plus plotting and diagnostic tools.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"GetDist provides automated kernel density estimation for weighted and correlated Monte Carlo samples with boundary corrections.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"394214f25cbb1ac9c336f1a1df591b280a602ca0797d43d52b68844969324cdf"},"source":{"id":"1910.13970","kind":"arxiv","version":2},"verdict":{"id":"498bae20-cca0-4503-9910-b5278e501f79","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T23:54:14.216260Z","strongest_claim":"GetDist's baseline method consists of applying a linear boundary kernel, and then using multiplicative bias correction. The smoothing bandwidth is selected automatically following Botev et al., based on a mixture of heuristics and optimization results using the expected scaling with an effective number of samples.","one_line_summary":"GetDist implements boundary-corrected KDE with automatic smoothing for analyzing weighted and correlated Monte Carlo samples, plus plotting and diagnostic tools.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the automatic bandwidth selection and linear boundary kernel produce accurate densities for the typical distributions and boundary conditions encountered in cosmological and physical parameter inference without requiring user tuning.","pith_extraction_headline":"GetDist provides automated kernel density estimation for weighted and correlated Monte Carlo samples with boundary corrections."},"references":{"count":54,"sample":[{"doi":"","year":null,"title":"This range excludes extreme outliers by spanning from the range_confidence quantile to the quantile of total weight 1 − range_confidence","work_id":"811971e7-65c7-48c1-8e5c-b6bff2b4a592","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"• If a prior boundary is well outside the initial sample range, it is ignored for the purpose of this range setting","work_id":"d4c1e25a-9b33-441e-a906-f8652843cfad","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"This KDE accounts for boundary effects from any active priors and is normalized so its peak value is one","work_id":"301fdb32-46d1-422a-9ca6-7e58ce7dbe49","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Significant Density","work_id":"ca634974-040f-4383-ae1a-2f012b8a4a52","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":null,"title":"Flags (marge_limits_bot, marge_limits_top) are set to indicate if the distribution appears significantly trun- cated by a boundary prior; e.g","work_id":"5e0ff6b3-adc7-4a14-97b6-655d2ed1fe67","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":54,"snapshot_sha256":"b491fd6e7ab32c2c6d6e80326e700aaac73789aa8a698fd64f13b22c76b19d05","internal_anchors":12},"formal_canon":{"evidence_count":2,"snapshot_sha256":"a862fcd28678d49db5f3538de9a3a9e6e8fa3f60ee2e3d5c7f83e6cdc185f587"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}