{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:YZOHV6XUXVGHV6GI2TRFII5PLI","short_pith_number":"pith:YZOHV6XU","schema_version":"1.0","canonical_sha256":"c65c7afaf4bd4c7af8c8d4e25423af5a193f9a46628fb49e958eeae7c7395fa7","source":{"kind":"arxiv","id":"1608.05347","version":1},"attestation_state":"computed","paper":{"title":"Probabilistic Data Analysis with Probabilistic Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Feras Saad, Vikash Mansinghka","submitted_at":"2016-08-18T17:47:53Z","abstract_excerpt":"Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bayesian models, multivariate kernel methods, discriminative machine learning, clustering algorithms, dimensionality reduction, and arbitrary probabilistic programs. We also demonstrate the integration of C"},"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":"1608.05347","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-08-18T17:47:53Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"76d3cdf403380863cc3a67c22b2d4030a3dbf42d179788c83920bda0f6476f9f","abstract_canon_sha256":"9b698f0b9f65760e9260720d204f8010eff11c2ccd033bc5a51d73b059adede5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:08:31.329308Z","signature_b64":"mVuEiLn6IRhhaMRCcq+KAXSef5KDW71lNT3FKG28TDgL6MSQmeidB2/oFpsH5Lr7hAF8G32U0WDWI4BAfvqyCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c65c7afaf4bd4c7af8c8d4e25423af5a193f9a46628fb49e958eeae7c7395fa7","last_reissued_at":"2026-05-18T01:08:31.328749Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:08:31.328749Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Probabilistic Data Analysis with Probabilistic Programming","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.AI","authors_text":"Feras Saad, Vikash Mansinghka","submitted_at":"2016-08-18T17:47:53Z","abstract_excerpt":"Probabilistic techniques are central to data analysis, but different approaches can be difficult to apply, combine, and compare. This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques. Examples include hierarchical Bayesian models, multivariate kernel methods, discriminative machine learning, clustering algorithms, dimensionality reduction, and arbitrary probabilistic programs. We also demonstrate the integration of C"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.05347","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":""},"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":"1608.05347","created_at":"2026-05-18T01:08:31.328825+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.05347v1","created_at":"2026-05-18T01:08:31.328825+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.05347","created_at":"2026-05-18T01:08:31.328825+00:00"},{"alias_kind":"pith_short_12","alias_value":"YZOHV6XUXVGH","created_at":"2026-05-18T12:30:53.716459+00:00"},{"alias_kind":"pith_short_16","alias_value":"YZOHV6XUXVGHV6GI","created_at":"2026-05-18T12:30:53.716459+00:00"},{"alias_kind":"pith_short_8","alias_value":"YZOHV6XU","created_at":"2026-05-18T12:30:53.716459+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"1907.06249","citing_title":"Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling","ref_index":22,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YZOHV6XUXVGHV6GI2TRFII5PLI","json":"https://pith.science/pith/YZOHV6XUXVGHV6GI2TRFII5PLI.json","graph_json":"https://pith.science/api/pith-number/YZOHV6XUXVGHV6GI2TRFII5PLI/graph.json","events_json":"https://pith.science/api/pith-number/YZOHV6XUXVGHV6GI2TRFII5PLI/events.json","paper":"https://pith.science/paper/YZOHV6XU"},"agent_actions":{"view_html":"https://pith.science/pith/YZOHV6XUXVGHV6GI2TRFII5PLI","download_json":"https://pith.science/pith/YZOHV6XUXVGHV6GI2TRFII5PLI.json","view_paper":"https://pith.science/paper/YZOHV6XU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.05347&json=true","fetch_graph":"https://pith.science/api/pith-number/YZOHV6XUXVGHV6GI2TRFII5PLI/graph.json","fetch_events":"https://pith.science/api/pith-number/YZOHV6XUXVGHV6GI2TRFII5PLI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YZOHV6XUXVGHV6GI2TRFII5PLI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YZOHV6XUXVGHV6GI2TRFII5PLI/action/storage_attestation","attest_author":"https://pith.science/pith/YZOHV6XUXVGHV6GI2TRFII5PLI/action/author_attestation","sign_citation":"https://pith.science/pith/YZOHV6XUXVGHV6GI2TRFII5PLI/action/citation_signature","submit_replication":"https://pith.science/pith/YZOHV6XUXVGHV6GI2TRFII5PLI/action/replication_record"}},"created_at":"2026-05-18T01:08:31.328825+00:00","updated_at":"2026-05-18T01:08:31.328825+00:00"}