{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:X7WHXWKHSEEKGEKPXLMFRMRYFR","short_pith_number":"pith:X7WHXWKH","schema_version":"1.0","canonical_sha256":"bfec7bd9479108a3114fbad858b2382c624382b67bcd4fa6bdc96fef8e5ae9a4","source":{"kind":"arxiv","id":"2202.02405","version":2},"attestation_state":"computed","paper":{"title":"BAM: Bayes with Adaptive Memory","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ben Evans, Jennifer Brennan, Josue Nassar, Kendall Lowrey","submitted_at":"2022-02-04T21:55:17Z","abstract_excerpt":"Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs. However, a naive application of Bayesian methods in non stationary environments leads to slow adaptation and results in state estimates that may converge confidently to the wrong parameter value. A common solution when learning in changing environments is to discard/downweight past data; however, this simple mechanism of \"forgetting\" fails to account for the fact that many real-world environments involve revisiting similar states. We propose a new framework, Bayes with Adaptive Mem"},"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":"2202.02405","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2022-02-04T21:55:17Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"3328d581a441bfc2112acf2b3d953167c2b11a854d83f1aa908c504f96c41c70","abstract_canon_sha256":"d7e9bfec18c6d9e4d6dfca4ffa3e717d2460a10c9939fd05d04895750750854e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:55:20.158091Z","signature_b64":"KomnAP1RMW6zSzKyyGPcH2l0Siw8RkHdkgMM/tDEs6N/PFOVpX99Y11M8EJlG3anjeL5cuidZB65xP4/GAWKCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bfec7bd9479108a3114fbad858b2382c624382b67bcd4fa6bdc96fef8e5ae9a4","last_reissued_at":"2026-07-05T03:55:20.157677Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:55:20.157677Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BAM: Bayes with Adaptive Memory","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ben Evans, Jennifer Brennan, Josue Nassar, Kendall Lowrey","submitted_at":"2022-02-04T21:55:17Z","abstract_excerpt":"Online learning via Bayes' theorem allows new data to be continuously integrated into an agent's current beliefs. However, a naive application of Bayesian methods in non stationary environments leads to slow adaptation and results in state estimates that may converge confidently to the wrong parameter value. A common solution when learning in changing environments is to discard/downweight past data; however, this simple mechanism of \"forgetting\" fails to account for the fact that many real-world environments involve revisiting similar states. We propose a new framework, Bayes with Adaptive Mem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2202.02405","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/2202.02405/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":"2202.02405","created_at":"2026-07-05T03:55:20.157738+00:00"},{"alias_kind":"arxiv_version","alias_value":"2202.02405v2","created_at":"2026-07-05T03:55:20.157738+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2202.02405","created_at":"2026-07-05T03:55:20.157738+00:00"},{"alias_kind":"pith_short_12","alias_value":"X7WHXWKHSEEK","created_at":"2026-07-05T03:55:20.157738+00:00"},{"alias_kind":"pith_short_16","alias_value":"X7WHXWKHSEEKGEKP","created_at":"2026-07-05T03:55:20.157738+00:00"},{"alias_kind":"pith_short_8","alias_value":"X7WHXWKH","created_at":"2026-07-05T03:55:20.157738+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/X7WHXWKHSEEKGEKPXLMFRMRYFR","json":"https://pith.science/pith/X7WHXWKHSEEKGEKPXLMFRMRYFR.json","graph_json":"https://pith.science/api/pith-number/X7WHXWKHSEEKGEKPXLMFRMRYFR/graph.json","events_json":"https://pith.science/api/pith-number/X7WHXWKHSEEKGEKPXLMFRMRYFR/events.json","paper":"https://pith.science/paper/X7WHXWKH"},"agent_actions":{"view_html":"https://pith.science/pith/X7WHXWKHSEEKGEKPXLMFRMRYFR","download_json":"https://pith.science/pith/X7WHXWKHSEEKGEKPXLMFRMRYFR.json","view_paper":"https://pith.science/paper/X7WHXWKH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2202.02405&json=true","fetch_graph":"https://pith.science/api/pith-number/X7WHXWKHSEEKGEKPXLMFRMRYFR/graph.json","fetch_events":"https://pith.science/api/pith-number/X7WHXWKHSEEKGEKPXLMFRMRYFR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/X7WHXWKHSEEKGEKPXLMFRMRYFR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/X7WHXWKHSEEKGEKPXLMFRMRYFR/action/storage_attestation","attest_author":"https://pith.science/pith/X7WHXWKHSEEKGEKPXLMFRMRYFR/action/author_attestation","sign_citation":"https://pith.science/pith/X7WHXWKHSEEKGEKPXLMFRMRYFR/action/citation_signature","submit_replication":"https://pith.science/pith/X7WHXWKHSEEKGEKPXLMFRMRYFR/action/replication_record"}},"created_at":"2026-07-05T03:55:20.157738+00:00","updated_at":"2026-07-05T03:55:20.157738+00:00"}