{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:4YRMMXS4FVIZ363Q6JRIIEW4JW","short_pith_number":"pith:4YRMMXS4","schema_version":"1.0","canonical_sha256":"e622c65e5c2d519dfb70f2628412dc4d80fdca54e9ea47f8ace040f5d7f107b1","source":{"kind":"arxiv","id":"1804.06748","version":3},"attestation_state":"computed","paper":{"title":"State-Space Abstractions for Probabilistic Inference: A Systematic Review","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Frank Kr\\\"uger, Max Schr\\\"oder, Sebastian Bader, Stefan L\\\"udtke, Thomas Kirste","submitted_at":"2018-04-18T14:10:10Z","abstract_excerpt":"Tasks such as social network analysis, human behavior recognition, or modeling biochemical reactions, can be solved elegantly by using the probabilistic inference framework. However, standard probabilistic inference algorithms work at a propositional level, and thus cannot capture the symmetries and redundancies that are present in these tasks. Algorithms that exploit those symmetries have been devised in different research fields, for example by the lifted inference-, multiple object tracking-, and modeling and simulation-communities. The common idea, that we call state space abstraction, is "},"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":"1804.06748","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2018-04-18T14:10:10Z","cross_cats_sorted":[],"title_canon_sha256":"4dc4beccac28d53d50c7b9b14f2ebc99f551d15b93972ef8e161f3f2026965a4","abstract_canon_sha256":"67e563cad1b0b41eefbf668976bd2a2789ef9944713dabe99b709d982ca19b4e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:58:48.079874Z","signature_b64":"W3DquJIO90E6sM82BYx0RMUi85z8K5tj63bbmQ/QGADtlQ3p2Lt7BEZMscPXe7eYSyfF3bqoKEj05U8gxcA4AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e622c65e5c2d519dfb70f2628412dc4d80fdca54e9ea47f8ace040f5d7f107b1","last_reissued_at":"2026-05-17T23:58:48.079517Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:58:48.079517Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"State-Space Abstractions for Probabilistic Inference: A Systematic Review","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Frank Kr\\\"uger, Max Schr\\\"oder, Sebastian Bader, Stefan L\\\"udtke, Thomas Kirste","submitted_at":"2018-04-18T14:10:10Z","abstract_excerpt":"Tasks such as social network analysis, human behavior recognition, or modeling biochemical reactions, can be solved elegantly by using the probabilistic inference framework. However, standard probabilistic inference algorithms work at a propositional level, and thus cannot capture the symmetries and redundancies that are present in these tasks. Algorithms that exploit those symmetries have been devised in different research fields, for example by the lifted inference-, multiple object tracking-, and modeling and simulation-communities. The common idea, that we call state space abstraction, is "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.06748","kind":"arxiv","version":3},"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":"1804.06748","created_at":"2026-05-17T23:58:48.079573+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.06748v3","created_at":"2026-05-17T23:58:48.079573+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.06748","created_at":"2026-05-17T23:58:48.079573+00:00"},{"alias_kind":"pith_short_12","alias_value":"4YRMMXS4FVIZ","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_16","alias_value":"4YRMMXS4FVIZ363Q","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_8","alias_value":"4YRMMXS4","created_at":"2026-05-18T12:32:05.422762+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4YRMMXS4FVIZ363Q6JRIIEW4JW","json":"https://pith.science/pith/4YRMMXS4FVIZ363Q6JRIIEW4JW.json","graph_json":"https://pith.science/api/pith-number/4YRMMXS4FVIZ363Q6JRIIEW4JW/graph.json","events_json":"https://pith.science/api/pith-number/4YRMMXS4FVIZ363Q6JRIIEW4JW/events.json","paper":"https://pith.science/paper/4YRMMXS4"},"agent_actions":{"view_html":"https://pith.science/pith/4YRMMXS4FVIZ363Q6JRIIEW4JW","download_json":"https://pith.science/pith/4YRMMXS4FVIZ363Q6JRIIEW4JW.json","view_paper":"https://pith.science/paper/4YRMMXS4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.06748&json=true","fetch_graph":"https://pith.science/api/pith-number/4YRMMXS4FVIZ363Q6JRIIEW4JW/graph.json","fetch_events":"https://pith.science/api/pith-number/4YRMMXS4FVIZ363Q6JRIIEW4JW/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4YRMMXS4FVIZ363Q6JRIIEW4JW/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4YRMMXS4FVIZ363Q6JRIIEW4JW/action/storage_attestation","attest_author":"https://pith.science/pith/4YRMMXS4FVIZ363Q6JRIIEW4JW/action/author_attestation","sign_citation":"https://pith.science/pith/4YRMMXS4FVIZ363Q6JRIIEW4JW/action/citation_signature","submit_replication":"https://pith.science/pith/4YRMMXS4FVIZ363Q6JRIIEW4JW/action/replication_record"}},"created_at":"2026-05-17T23:58:48.079573+00:00","updated_at":"2026-05-17T23:58:48.079573+00:00"}