{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ZT5CRDFJLASMVNRPWTDISP5E64","short_pith_number":"pith:ZT5CRDFJ","schema_version":"1.0","canonical_sha256":"ccfa288ca95824cab62fb4c6893fa4f73b5e0220e59de1ec936905caa233903d","source":{"kind":"arxiv","id":"2306.16923","version":1},"attestation_state":"computed","paper":{"title":"NAUTILUS: boosting Bayesian importance nested sampling with deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.CO","astro-ph.EP","astro-ph.GA","cs.LG"],"primary_cat":"astro-ph.IM","authors_text":"Johannes U. Lange","submitted_at":"2023-06-29T13:18:57Z","abstract_excerpt":"We introduce a novel approach to boost the efficiency of the importance nested sampling (INS) technique for Bayesian posterior and evidence estimation using deep learning. Unlike rejection-based sampling methods such as vanilla nested sampling (NS) or Markov chain Monte Carlo (MCMC) algorithms, importance sampling techniques can use all likelihood evaluations for posterior and evidence estimation. However, for efficient importance sampling, one needs proposal distributions that closely mimic the posterior distributions. We show how to combine INS with deep learning via neural network regressio"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2306.16923","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"astro-ph.IM","submitted_at":"2023-06-29T13:18:57Z","cross_cats_sorted":["astro-ph.CO","astro-ph.EP","astro-ph.GA","cs.LG"],"title_canon_sha256":"774c47741ec6df252bc2afde7d5a6f2f1f2d02545ffa41d78f4c4f692ea00f3d","abstract_canon_sha256":"248c4240858e7976a8a9a65503b3b8e557a1f4aabd83fb2900d3a210f15858ec"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:26:11.050375Z","signature_b64":"DnrAVBmIEV4Dk6ZKx06X59mDob2a5Gn2pif3DLzGHF1Luy+GtRgksPfwa188OmgdbEtkq1wXUoAyehIROvxsBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ccfa288ca95824cab62fb4c6893fa4f73b5e0220e59de1ec936905caa233903d","last_reissued_at":"2026-07-05T06:26:11.049967Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:26:11.049967Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"NAUTILUS: boosting Bayesian importance nested sampling with deep learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.CO","astro-ph.EP","astro-ph.GA","cs.LG"],"primary_cat":"astro-ph.IM","authors_text":"Johannes U. Lange","submitted_at":"2023-06-29T13:18:57Z","abstract_excerpt":"We introduce a novel approach to boost the efficiency of the importance nested sampling (INS) technique for Bayesian posterior and evidence estimation using deep learning. Unlike rejection-based sampling methods such as vanilla nested sampling (NS) or Markov chain Monte Carlo (MCMC) algorithms, importance sampling techniques can use all likelihood evaluations for posterior and evidence estimation. However, for efficient importance sampling, one needs proposal distributions that closely mimic the posterior distributions. We show how to combine INS with deep learning via neural network regressio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.16923","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/2306.16923/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2306.16923","created_at":"2026-07-05T06:26:11.050029+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.16923v1","created_at":"2026-07-05T06:26:11.050029+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.16923","created_at":"2026-07-05T06:26:11.050029+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZT5CRDFJLASM","created_at":"2026-07-05T06:26:11.050029+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZT5CRDFJLASMVNRP","created_at":"2026-07-05T06:26:11.050029+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZT5CRDFJ","created_at":"2026-07-05T06:26:11.050029+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":2,"sample":[{"citing_arxiv_id":"2607.06697","citing_title":"Impact and measurability of linear relativistic effects in galaxy surveys","ref_index":141,"is_internal_anchor":true},{"citing_arxiv_id":"2607.06844","citing_title":"First detection of ultra-fast outflows in a quiescent galaxy","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"2605.27221","citing_title":"Constraints on Dynamical Dark Energy from Multiple Probes in the Full Dark Energy Survey","ref_index":64,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25373","citing_title":"Generalizing the CPL Parametrization through Dark Sector Interaction","ref_index":84,"is_internal_anchor":false},{"citing_arxiv_id":"2604.25373","citing_title":"Generalizing the CPL Parametrization through Dark Sector Interaction","ref_index":84,"is_internal_anchor":false},{"citing_arxiv_id":"2605.05512","citing_title":"Testing General Relativity with Individual Supermassive Black Hole Binaries","ref_index":86,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZT5CRDFJLASMVNRPWTDISP5E64","json":"https://pith.science/pith/ZT5CRDFJLASMVNRPWTDISP5E64.json","graph_json":"https://pith.science/api/pith-number/ZT5CRDFJLASMVNRPWTDISP5E64/graph.json","events_json":"https://pith.science/api/pith-number/ZT5CRDFJLASMVNRPWTDISP5E64/events.json","paper":"https://pith.science/paper/ZT5CRDFJ"},"agent_actions":{"view_html":"https://pith.science/pith/ZT5CRDFJLASMVNRPWTDISP5E64","download_json":"https://pith.science/pith/ZT5CRDFJLASMVNRPWTDISP5E64.json","view_paper":"https://pith.science/paper/ZT5CRDFJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.16923&json=true","fetch_graph":"https://pith.science/api/pith-number/ZT5CRDFJLASMVNRPWTDISP5E64/graph.json","fetch_events":"https://pith.science/api/pith-number/ZT5CRDFJLASMVNRPWTDISP5E64/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZT5CRDFJLASMVNRPWTDISP5E64/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZT5CRDFJLASMVNRPWTDISP5E64/action/storage_attestation","attest_author":"https://pith.science/pith/ZT5CRDFJLASMVNRPWTDISP5E64/action/author_attestation","sign_citation":"https://pith.science/pith/ZT5CRDFJLASMVNRPWTDISP5E64/action/citation_signature","submit_replication":"https://pith.science/pith/ZT5CRDFJLASMVNRPWTDISP5E64/action/replication_record"}},"created_at":"2026-07-05T06:26:11.050029+00:00","updated_at":"2026-07-05T06:26:11.050029+00:00"}