{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:YOGLNNRLVAPMU7PRIKRTPO6KXK","short_pith_number":"pith:YOGLNNRL","schema_version":"1.0","canonical_sha256":"c38cb6b62ba81eca7df142a337bbcaba95e69ac81fa95810331d17a5e5fe7043","source":{"kind":"arxiv","id":"1212.2497","version":1},"attestation_state":"computed","paper":{"title":"Solving MAP Exactly using Systematic Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Adnan Darwiche, James D. Park","submitted_at":"2012-10-19T15:07:27Z","abstract_excerpt":"MAP is the problem of finding a most probable instantiation of a set of     variables in a Bayesian network given some evidence. Unlike computing posterior     probabilities, or MPE (a special case of MAP), the time and space complexity of     structural solutions for MAP are not only exponential in the network treewidth,     but in a larger parameter known as the \"constrained\" treewidth. In practice,     this means that computing MAP can be orders of magnitude more expensive than     computing posterior probabilities or MPE. This paper introduces a new, simple     upper bound on the probabili"},"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":"1212.2497","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2012-10-19T15:07:27Z","cross_cats_sorted":[],"title_canon_sha256":"c7f122906effe071d45ec6a67b7ac98abe773b5b401814a385cf2500c376a847","abstract_canon_sha256":"3d4ded2cbb679ac70d22a3ba8e24a9d649d9136862c5207df775dba845ab8209"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:38:44.546215Z","signature_b64":"7BdlixJlQsDASUokIzvN1j21GzX5VevaEllSyxuu5oMVABt54fyerRW9tF8NJ4PaQQdxG93Zz0arPs8NkwhaDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c38cb6b62ba81eca7df142a337bbcaba95e69ac81fa95810331d17a5e5fe7043","last_reissued_at":"2026-05-18T03:38:44.545584Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:38:44.545584Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Solving MAP Exactly using Systematic Search","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Adnan Darwiche, James D. Park","submitted_at":"2012-10-19T15:07:27Z","abstract_excerpt":"MAP is the problem of finding a most probable instantiation of a set of     variables in a Bayesian network given some evidence. Unlike computing posterior     probabilities, or MPE (a special case of MAP), the time and space complexity of     structural solutions for MAP are not only exponential in the network treewidth,     but in a larger parameter known as the \"constrained\" treewidth. In practice,     this means that computing MAP can be orders of magnitude more expensive than     computing posterior probabilities or MPE. This paper introduces a new, simple     upper bound on the probabili"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.2497","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":"1212.2497","created_at":"2026-05-18T03:38:44.545692+00:00"},{"alias_kind":"arxiv_version","alias_value":"1212.2497v1","created_at":"2026-05-18T03:38:44.545692+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1212.2497","created_at":"2026-05-18T03:38:44.545692+00:00"},{"alias_kind":"pith_short_12","alias_value":"YOGLNNRLVAPM","created_at":"2026-05-18T12:27:27.928770+00:00"},{"alias_kind":"pith_short_16","alias_value":"YOGLNNRLVAPMU7PR","created_at":"2026-05-18T12:27:27.928770+00:00"},{"alias_kind":"pith_short_8","alias_value":"YOGLNNRL","created_at":"2026-05-18T12:27:27.928770+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2507.19338","citing_title":"Branch-and-bound method for calculating Viterbi path in triplet Markov models","ref_index":30,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YOGLNNRLVAPMU7PRIKRTPO6KXK","json":"https://pith.science/pith/YOGLNNRLVAPMU7PRIKRTPO6KXK.json","graph_json":"https://pith.science/api/pith-number/YOGLNNRLVAPMU7PRIKRTPO6KXK/graph.json","events_json":"https://pith.science/api/pith-number/YOGLNNRLVAPMU7PRIKRTPO6KXK/events.json","paper":"https://pith.science/paper/YOGLNNRL"},"agent_actions":{"view_html":"https://pith.science/pith/YOGLNNRLVAPMU7PRIKRTPO6KXK","download_json":"https://pith.science/pith/YOGLNNRLVAPMU7PRIKRTPO6KXK.json","view_paper":"https://pith.science/paper/YOGLNNRL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1212.2497&json=true","fetch_graph":"https://pith.science/api/pith-number/YOGLNNRLVAPMU7PRIKRTPO6KXK/graph.json","fetch_events":"https://pith.science/api/pith-number/YOGLNNRLVAPMU7PRIKRTPO6KXK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YOGLNNRLVAPMU7PRIKRTPO6KXK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YOGLNNRLVAPMU7PRIKRTPO6KXK/action/storage_attestation","attest_author":"https://pith.science/pith/YOGLNNRLVAPMU7PRIKRTPO6KXK/action/author_attestation","sign_citation":"https://pith.science/pith/YOGLNNRLVAPMU7PRIKRTPO6KXK/action/citation_signature","submit_replication":"https://pith.science/pith/YOGLNNRLVAPMU7PRIKRTPO6KXK/action/replication_record"}},"created_at":"2026-05-18T03:38:44.545692+00:00","updated_at":"2026-05-18T03:38:44.545692+00:00"}