{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ILPSXNFHK472M2JUYXRKJZWSMP","short_pith_number":"pith:ILPSXNFH","schema_version":"1.0","canonical_sha256":"42df2bb4a7573fa66934c5e2a4e6d263f7bdee56320d5b3e4acb54365791d21f","source":{"kind":"arxiv","id":"2306.12584","version":2},"attestation_state":"computed","paper":{"title":"Hierarchical Neural Simulation-Based Inference Over Event Ensembles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM","cs.LG","hep-ex"],"primary_cat":"stat.ML","authors_text":"Chris Pollard, Lukas Heinrich, Philipp Windischhofer, Siddharth Mishra-Sharma","submitted_at":"2023-06-21T21:50:42Z","abstract_excerpt":"When analyzing real-world data it is common to work with event ensembles, which comprise sets of observations that collectively constrain the parameters of an underlying model of interest. Such models often have a hierarchical structure, where \"local\" parameters impact individual events and \"global\" parameters influence the entire dataset. We introduce practical approaches for frequentist and Bayesian dataset-wide probabilistic inference in cases where the likelihood is intractable, but simulations can be realized via a hierarchical forward model. We construct neural estimators for the likelih"},"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":"2306.12584","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2023-06-21T21:50:42Z","cross_cats_sorted":["astro-ph.IM","cs.LG","hep-ex"],"title_canon_sha256":"810c1c0abf81d159f7d13c7edf3280a91bb4d220c12e1b42bdd40b3a13d422f6","abstract_canon_sha256":"b2cc201bfed77bf070fa30db15531838ca9b9c12bdafc2b5599dcf8ad80b4a5c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:43.542565Z","signature_b64":"zUACtihRaB9IV3Xsidyj9I8MulNvhAjUF7Fv4iQBxjYS9sazrO5GhTBVeBV/SufpFLKx0lOZBW6lJme5r8U/CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"42df2bb4a7573fa66934c5e2a4e6d263f7bdee56320d5b3e4acb54365791d21f","last_reissued_at":"2026-07-05T07:47:43.542043Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:43.542043Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hierarchical Neural Simulation-Based Inference Over Event Ensembles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM","cs.LG","hep-ex"],"primary_cat":"stat.ML","authors_text":"Chris Pollard, Lukas Heinrich, Philipp Windischhofer, Siddharth Mishra-Sharma","submitted_at":"2023-06-21T21:50:42Z","abstract_excerpt":"When analyzing real-world data it is common to work with event ensembles, which comprise sets of observations that collectively constrain the parameters of an underlying model of interest. Such models often have a hierarchical structure, where \"local\" parameters impact individual events and \"global\" parameters influence the entire dataset. We introduce practical approaches for frequentist and Bayesian dataset-wide probabilistic inference in cases where the likelihood is intractable, but simulations can be realized via a hierarchical forward model. We construct neural estimators for the likelih"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.12584","kind":"arxiv","version":2},"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/2306.12584/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":"2306.12584","created_at":"2026-07-05T07:47:43.542115+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.12584v2","created_at":"2026-07-05T07:47:43.542115+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.12584","created_at":"2026-07-05T07:47:43.542115+00:00"},{"alias_kind":"pith_short_12","alias_value":"ILPSXNFHK472","created_at":"2026-07-05T07:47:43.542115+00:00"},{"alias_kind":"pith_short_16","alias_value":"ILPSXNFHK472M2JU","created_at":"2026-07-05T07:47:43.542115+00:00"},{"alias_kind":"pith_short_8","alias_value":"ILPSXNFH","created_at":"2026-07-05T07:47:43.542115+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.07972","citing_title":"It Just Takes Two: Scaling Amortized Inference to Large Sets","ref_index":2,"is_internal_anchor":false},{"citing_arxiv_id":"2604.20723","citing_title":"Tokenised Flow Matching for Hierarchical Simulation Based Inference","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ILPSXNFHK472M2JUYXRKJZWSMP","json":"https://pith.science/pith/ILPSXNFHK472M2JUYXRKJZWSMP.json","graph_json":"https://pith.science/api/pith-number/ILPSXNFHK472M2JUYXRKJZWSMP/graph.json","events_json":"https://pith.science/api/pith-number/ILPSXNFHK472M2JUYXRKJZWSMP/events.json","paper":"https://pith.science/paper/ILPSXNFH"},"agent_actions":{"view_html":"https://pith.science/pith/ILPSXNFHK472M2JUYXRKJZWSMP","download_json":"https://pith.science/pith/ILPSXNFHK472M2JUYXRKJZWSMP.json","view_paper":"https://pith.science/paper/ILPSXNFH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.12584&json=true","fetch_graph":"https://pith.science/api/pith-number/ILPSXNFHK472M2JUYXRKJZWSMP/graph.json","fetch_events":"https://pith.science/api/pith-number/ILPSXNFHK472M2JUYXRKJZWSMP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ILPSXNFHK472M2JUYXRKJZWSMP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ILPSXNFHK472M2JUYXRKJZWSMP/action/storage_attestation","attest_author":"https://pith.science/pith/ILPSXNFHK472M2JUYXRKJZWSMP/action/author_attestation","sign_citation":"https://pith.science/pith/ILPSXNFHK472M2JUYXRKJZWSMP/action/citation_signature","submit_replication":"https://pith.science/pith/ILPSXNFHK472M2JUYXRKJZWSMP/action/replication_record"}},"created_at":"2026-07-05T07:47:43.542115+00:00","updated_at":"2026-07-05T07:47:43.542115+00:00"}