{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:GPXKWZFD67T7Q4RSP4QTWXYNYZ","short_pith_number":"pith:GPXKWZFD","schema_version":"1.0","canonical_sha256":"33eeab64a3f7e7f872327f213b5f0dc66786ebaa35ea96da392924dbde2f9865","source":{"kind":"arxiv","id":"1604.02737","version":2},"attestation_state":"computed","paper":{"title":"Correlated Equilibria for Approximate Variational Inference in MRFs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GT","stat.ML"],"primary_cat":"cs.AI","authors_text":"Boshen Wang, Luis E. Ortiz, Ze Gong","submitted_at":"2016-04-10T21:21:00Z","abstract_excerpt":"Almost all of the work in graphical models for game theory has mirrored previous work in probabilistic graphical models. Our work considers the opposite direction: Taking advantage of recent advances in equilibrium computation for probabilistic inference. We present formulations of inference problems in Markov random fields (MRFs) as computation of equilibria in a certain class of game-theoretic graphical models. We concretely establishes the precise connection between variational probabilistic inference in MRFs and correlated equilibria. No previous work exploits recent theoretical and empiri"},"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":"1604.02737","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2016-04-10T21:21:00Z","cross_cats_sorted":["cs.GT","stat.ML"],"title_canon_sha256":"728d648dfac10ea2e7f01e5ec196ea4763978b96f43fbf9a30d5a048d8fdf1fc","abstract_canon_sha256":"e7905422770c1ce17203616f460a27096601a2372d4fbe6b0d1cff07adc32571"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:33:32.564366Z","signature_b64":"A60v68nyyOOF56hzYRfV15Ys6KEZsaIJWfLn3IPhPQyaH8kkLhDvl+wkyjHq6DpJrvpfq9esOGFwKmIFpDjeBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"33eeab64a3f7e7f872327f213b5f0dc66786ebaa35ea96da392924dbde2f9865","last_reissued_at":"2026-05-18T00:33:32.563807Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:33:32.563807Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Correlated Equilibria for Approximate Variational Inference in MRFs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.GT","stat.ML"],"primary_cat":"cs.AI","authors_text":"Boshen Wang, Luis E. Ortiz, Ze Gong","submitted_at":"2016-04-10T21:21:00Z","abstract_excerpt":"Almost all of the work in graphical models for game theory has mirrored previous work in probabilistic graphical models. Our work considers the opposite direction: Taking advantage of recent advances in equilibrium computation for probabilistic inference. We present formulations of inference problems in Markov random fields (MRFs) as computation of equilibria in a certain class of game-theoretic graphical models. We concretely establishes the precise connection between variational probabilistic inference in MRFs and correlated equilibria. No previous work exploits recent theoretical and empiri"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1604.02737","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":""},"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":"1604.02737","created_at":"2026-05-18T00:33:32.563895+00:00"},{"alias_kind":"arxiv_version","alias_value":"1604.02737v2","created_at":"2026-05-18T00:33:32.563895+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1604.02737","created_at":"2026-05-18T00:33:32.563895+00:00"},{"alias_kind":"pith_short_12","alias_value":"GPXKWZFD67T7","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_16","alias_value":"GPXKWZFD67T7Q4RS","created_at":"2026-05-18T12:30:19.053100+00:00"},{"alias_kind":"pith_short_8","alias_value":"GPXKWZFD","created_at":"2026-05-18T12:30:19.053100+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/GPXKWZFD67T7Q4RSP4QTWXYNYZ","json":"https://pith.science/pith/GPXKWZFD67T7Q4RSP4QTWXYNYZ.json","graph_json":"https://pith.science/api/pith-number/GPXKWZFD67T7Q4RSP4QTWXYNYZ/graph.json","events_json":"https://pith.science/api/pith-number/GPXKWZFD67T7Q4RSP4QTWXYNYZ/events.json","paper":"https://pith.science/paper/GPXKWZFD"},"agent_actions":{"view_html":"https://pith.science/pith/GPXKWZFD67T7Q4RSP4QTWXYNYZ","download_json":"https://pith.science/pith/GPXKWZFD67T7Q4RSP4QTWXYNYZ.json","view_paper":"https://pith.science/paper/GPXKWZFD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1604.02737&json=true","fetch_graph":"https://pith.science/api/pith-number/GPXKWZFD67T7Q4RSP4QTWXYNYZ/graph.json","fetch_events":"https://pith.science/api/pith-number/GPXKWZFD67T7Q4RSP4QTWXYNYZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GPXKWZFD67T7Q4RSP4QTWXYNYZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GPXKWZFD67T7Q4RSP4QTWXYNYZ/action/storage_attestation","attest_author":"https://pith.science/pith/GPXKWZFD67T7Q4RSP4QTWXYNYZ/action/author_attestation","sign_citation":"https://pith.science/pith/GPXKWZFD67T7Q4RSP4QTWXYNYZ/action/citation_signature","submit_replication":"https://pith.science/pith/GPXKWZFD67T7Q4RSP4QTWXYNYZ/action/replication_record"}},"created_at":"2026-05-18T00:33:32.563895+00:00","updated_at":"2026-05-18T00:33:32.563895+00:00"}