{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:ZV7HJXGS4WAG5ZASU2RQCCAHIX","short_pith_number":"pith:ZV7HJXGS","schema_version":"1.0","canonical_sha256":"cd7e74dcd2e5806ee412a6a301080745c34aaa7fdff70f925b09cc800d419677","source":{"kind":"arxiv","id":"1807.06481","version":2},"attestation_state":"computed","paper":{"title":"Dynamic Sampling from Graphical Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.DS","authors_text":"Nisheeth K. Vishnoi, Weiming Feng, Yitong Yin","submitted_at":"2018-07-17T14:54:06Z","abstract_excerpt":"In this paper, we study the problem of sampling from a graphical model when the model itself is changing dynamically with time. This problem derives its interest from a variety of inference, learning, and sampling settings in machine learning, computer vision, statistical physics, and theoretical computer science. While the problem of sampling from a static graphical model has received considerable attention, theoretical works for its dynamic variants have been largely lacking. The main contribution of this paper is an algorithm that can sample dynamically from a broad class of graphical model"},"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":"1807.06481","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DS","submitted_at":"2018-07-17T14:54:06Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"43b18a9065e99000df5cd88be43f617ed95e56e27f525f86b88e1618bbd1d4fa","abstract_canon_sha256":"9a9506bf64d41e1bee469c7d1a1ff913e8b7de46e13aaa43ff023bbcb46f24a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:00:43.194364Z","signature_b64":"voK6crnnoFkxYe5cC/Ge5CcXiSnYRvqPqEpR9tOI2tZ7uZlj4WlRO/Qz2obFeEMYnNsDXX0q6NID9PO9LxRdCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cd7e74dcd2e5806ee412a6a301080745c34aaa7fdff70f925b09cc800d419677","last_reissued_at":"2026-05-18T00:00:43.193818Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:00:43.193818Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Dynamic Sampling from Graphical Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.DS","authors_text":"Nisheeth K. Vishnoi, Weiming Feng, Yitong Yin","submitted_at":"2018-07-17T14:54:06Z","abstract_excerpt":"In this paper, we study the problem of sampling from a graphical model when the model itself is changing dynamically with time. This problem derives its interest from a variety of inference, learning, and sampling settings in machine learning, computer vision, statistical physics, and theoretical computer science. While the problem of sampling from a static graphical model has received considerable attention, theoretical works for its dynamic variants have been largely lacking. The main contribution of this paper is an algorithm that can sample dynamically from a broad class of graphical model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.06481","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":"1807.06481","created_at":"2026-05-18T00:00:43.193947+00:00"},{"alias_kind":"arxiv_version","alias_value":"1807.06481v2","created_at":"2026-05-18T00:00:43.193947+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.06481","created_at":"2026-05-18T00:00:43.193947+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZV7HJXGS4WAG","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZV7HJXGS4WAG5ZAS","created_at":"2026-05-18T12:33:07.085635+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZV7HJXGS","created_at":"2026-05-18T12:33:07.085635+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/ZV7HJXGS4WAG5ZASU2RQCCAHIX","json":"https://pith.science/pith/ZV7HJXGS4WAG5ZASU2RQCCAHIX.json","graph_json":"https://pith.science/api/pith-number/ZV7HJXGS4WAG5ZASU2RQCCAHIX/graph.json","events_json":"https://pith.science/api/pith-number/ZV7HJXGS4WAG5ZASU2RQCCAHIX/events.json","paper":"https://pith.science/paper/ZV7HJXGS"},"agent_actions":{"view_html":"https://pith.science/pith/ZV7HJXGS4WAG5ZASU2RQCCAHIX","download_json":"https://pith.science/pith/ZV7HJXGS4WAG5ZASU2RQCCAHIX.json","view_paper":"https://pith.science/paper/ZV7HJXGS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1807.06481&json=true","fetch_graph":"https://pith.science/api/pith-number/ZV7HJXGS4WAG5ZASU2RQCCAHIX/graph.json","fetch_events":"https://pith.science/api/pith-number/ZV7HJXGS4WAG5ZASU2RQCCAHIX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZV7HJXGS4WAG5ZASU2RQCCAHIX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZV7HJXGS4WAG5ZASU2RQCCAHIX/action/storage_attestation","attest_author":"https://pith.science/pith/ZV7HJXGS4WAG5ZASU2RQCCAHIX/action/author_attestation","sign_citation":"https://pith.science/pith/ZV7HJXGS4WAG5ZASU2RQCCAHIX/action/citation_signature","submit_replication":"https://pith.science/pith/ZV7HJXGS4WAG5ZASU2RQCCAHIX/action/replication_record"}},"created_at":"2026-05-18T00:00:43.193947+00:00","updated_at":"2026-05-18T00:00:43.193947+00:00"}