{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:7MPJFZG6XGQD6A4IU5H54XWDMK","short_pith_number":"pith:7MPJFZG6","canonical_record":{"source":{"id":"1910.13970","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2019-10-30T16:38:10Z","cross_cats_sorted":["astro-ph.CO","physics.data-an"],"title_canon_sha256":"ab20242cf6aa171f91c0446bf68ecceb9792f859eaea7612aeb412f778febbba","abstract_canon_sha256":"c61b7fd933556eb94a2166e1d0cb39fe53112eb29c18ae1dbf41c2d3d45ae9b6"},"schema_version":"1.0"},"canonical_sha256":"fb1e92e4deb9a03f0388a74fde5ec362bf45e1fa07ec0de1368bce5161ed3c70","source":{"kind":"arxiv","id":"1910.13970","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.13970","created_at":"2026-05-17T23:39:19Z"},{"alias_kind":"arxiv_version","alias_value":"1910.13970v2","created_at":"2026-05-17T23:39:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.13970","created_at":"2026-05-17T23:39:19Z"},{"alias_kind":"pith_short_12","alias_value":"7MPJFZG6XGQD","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"7MPJFZG6XGQD6A4I","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"7MPJFZG6","created_at":"2026-05-18T12:33:12Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:7MPJFZG6XGQD6A4IU5H54XWDMK","target":"record","payload":{"canonical_record":{"source":{"id":"1910.13970","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2019-10-30T16:38:10Z","cross_cats_sorted":["astro-ph.CO","physics.data-an"],"title_canon_sha256":"ab20242cf6aa171f91c0446bf68ecceb9792f859eaea7612aeb412f778febbba","abstract_canon_sha256":"c61b7fd933556eb94a2166e1d0cb39fe53112eb29c18ae1dbf41c2d3d45ae9b6"},"schema_version":"1.0"},"canonical_sha256":"fb1e92e4deb9a03f0388a74fde5ec362bf45e1fa07ec0de1368bce5161ed3c70","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:19.781054Z","signature_b64":"M+HjYf9URxGd1FxIPq+2HKYjN1RSBLUnAs+C+JQucIy8sVhZozR58mtTRu1Uow/iW4JO8xm7AD3nH4k3ow/+Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fb1e92e4deb9a03f0388a74fde5ec362bf45e1fa07ec0de1368bce5161ed3c70","last_reissued_at":"2026-05-17T23:39:19.780395Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:19.780395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.13970","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"IfwOhOtcvIHDLPQCfl+k+1BPUBp25VSdNUSM3SGAsNlmfK2mjDYJHdcC3tpwWPiIygr0tgfw2vfJzSdHr3kJAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T14:14:50.244697Z"},"content_sha256":"b352ed50f0846698b1521e5949840db4a5ba790c21ff430150eecd8c60cdc157","schema_version":"1.0","event_id":"sha256:b352ed50f0846698b1521e5949840db4a5ba790c21ff430150eecd8c60cdc157"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:7MPJFZG6XGQD6A4IU5H54XWDMK","target":"graph","payload":{"graph_snapshot":{"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"},"verdict_id":"498bae20-cca0-4503-9910-b5278e501f79"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pmolkUoqzzHUwvvxnqR0k9V4ECH5ThnBxJxq577oj0K9aYbJj89au4/Oonw+js7wtLi41O78bKaL/yHPKofBAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T14:14:50.245696Z"},"content_sha256":"edbad8ee354be1306d0982b16a735bd6c64abe64ccc30a11db1f0650b232ef15","schema_version":"1.0","event_id":"sha256:edbad8ee354be1306d0982b16a735bd6c64abe64ccc30a11db1f0650b232ef15"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7MPJFZG6XGQD6A4IU5H54XWDMK/bundle.json","state_url":"https://pith.science/pith/7MPJFZG6XGQD6A4IU5H54XWDMK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7MPJFZG6XGQD6A4IU5H54XWDMK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-06T14:14:50Z","links":{"resolver":"https://pith.science/pith/7MPJFZG6XGQD6A4IU5H54XWDMK","bundle":"https://pith.science/pith/7MPJFZG6XGQD6A4IU5H54XWDMK/bundle.json","state":"https://pith.science/pith/7MPJFZG6XGQD6A4IU5H54XWDMK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7MPJFZG6XGQD6A4IU5H54XWDMK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:7MPJFZG6XGQD6A4IU5H54XWDMK","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"c61b7fd933556eb94a2166e1d0cb39fe53112eb29c18ae1dbf41c2d3d45ae9b6","cross_cats_sorted":["astro-ph.CO","physics.data-an"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2019-10-30T16:38:10Z","title_canon_sha256":"ab20242cf6aa171f91c0446bf68ecceb9792f859eaea7612aeb412f778febbba"},"schema_version":"1.0","source":{"id":"1910.13970","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.13970","created_at":"2026-05-17T23:39:19Z"},{"alias_kind":"arxiv_version","alias_value":"1910.13970v2","created_at":"2026-05-17T23:39:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.13970","created_at":"2026-05-17T23:39:19Z"},{"alias_kind":"pith_short_12","alias_value":"7MPJFZG6XGQD","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_16","alias_value":"7MPJFZG6XGQD6A4I","created_at":"2026-05-18T12:33:12Z"},{"alias_kind":"pith_short_8","alias_value":"7MPJFZG6","created_at":"2026-05-18T12:33:12Z"}],"graph_snapshots":[{"event_id":"sha256:edbad8ee354be1306d0982b16a735bd6c64abe64ccc30a11db1f0650b232ef15","target":"graph","created_at":"2026-05-17T23:39:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","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."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","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."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"GetDist implements boundary-corrected KDE with automatic smoothing for analyzing weighted and correlated Monte Carlo samples, plus plotting and diagnostic tools."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"GetDist provides automated kernel density estimation for weighted and correlated Monte Carlo samples with boundary corrections."}],"snapshot_sha256":"394214f25cbb1ac9c336f1a1df591b280a602ca0797d43d52b68844969324cdf"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"a862fcd28678d49db5f3538de9a3a9e6e8fa3f60ee2e3d5c7f83e6cdc185f587"},"paper":{"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","authors_text":"Antony Lewis","cross_cats":["astro-ph.CO","physics.data-an"],"headline":"GetDist provides automated kernel density estimation for weighted and correlated Monte Carlo samples with boundary corrections.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2019-10-30T16:38:10Z","title":"GetDist: a Python package for analysing Monte Carlo samples"},"references":{"count":54,"internal_anchors":12,"resolved_work":54,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"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","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"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","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"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","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Significant Density","work_id":"ca634974-040f-4383-ae1a-2f012b8a4a52","year":null},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"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","year":null}],"snapshot_sha256":"b491fd6e7ab32c2c6d6e80326e700aaac73789aa8a698fd64f13b22c76b19d05"},"source":{"id":"1910.13970","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-14T23:54:14.216260Z","id":"498bae20-cca0-4503-9910-b5278e501f79","model_set":{"reader":"grok-4.3"},"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","pith_extraction_headline":"GetDist provides automated kernel density estimation for weighted and correlated Monte Carlo samples with boundary corrections.","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.","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."}},"verdict_id":"498bae20-cca0-4503-9910-b5278e501f79"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b352ed50f0846698b1521e5949840db4a5ba790c21ff430150eecd8c60cdc157","target":"record","created_at":"2026-05-17T23:39:19Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"c61b7fd933556eb94a2166e1d0cb39fe53112eb29c18ae1dbf41c2d3d45ae9b6","cross_cats_sorted":["astro-ph.CO","physics.data-an"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2019-10-30T16:38:10Z","title_canon_sha256":"ab20242cf6aa171f91c0446bf68ecceb9792f859eaea7612aeb412f778febbba"},"schema_version":"1.0","source":{"id":"1910.13970","kind":"arxiv","version":2}},"canonical_sha256":"fb1e92e4deb9a03f0388a74fde5ec362bf45e1fa07ec0de1368bce5161ed3c70","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fb1e92e4deb9a03f0388a74fde5ec362bf45e1fa07ec0de1368bce5161ed3c70","first_computed_at":"2026-05-17T23:39:19.780395Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:19.780395Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"M+HjYf9URxGd1FxIPq+2HKYjN1RSBLUnAs+C+JQucIy8sVhZozR58mtTRu1Uow/iW4JO8xm7AD3nH4k3ow/+Bw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:19.781054Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.13970","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b352ed50f0846698b1521e5949840db4a5ba790c21ff430150eecd8c60cdc157","sha256:edbad8ee354be1306d0982b16a735bd6c64abe64ccc30a11db1f0650b232ef15"],"state_sha256":"0a65f69d074e19075b01c46a9fb34e1a5a08d4e1756b3f4e8183a1c11350be9f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y9HNOD8ZcUMiJzBDXeHxS3ATVCooxKOZx3L86eLb6HC2whUxs6+FPe3j0Tlf7HOuBraPNHCJGR+mQdE48OeMDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T14:14:50.250040Z","bundle_sha256":"08577bd53530696d1ab29da8fa31260939ff2b8f9fb3f43abf6cfe3cf87e942f"}}