{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:ICKJN2TPU3FH7Y4KTHPZIKK52U","short_pith_number":"pith:ICKJN2TP","canonical_record":{"source":{"id":"1201.4002","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-01-19T10:06:29Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"1521a0498fcc00d097754580e060e49e7d4198e1a146dd161baae018a0d9e5e6","abstract_canon_sha256":"e83afb3b0f927cf1f5558d41e9f3d0f192a5a311a903885865808ee602e3f157"},"schema_version":"1.0"},"canonical_sha256":"409496ea6fa6ca7fe38a99df94295dd5026dbc22064623d93b3f562615dbb149","source":{"kind":"arxiv","id":"1201.4002","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1201.4002","created_at":"2026-05-18T04:04:16Z"},{"alias_kind":"arxiv_version","alias_value":"1201.4002v1","created_at":"2026-05-18T04:04:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1201.4002","created_at":"2026-05-18T04:04:16Z"},{"alias_kind":"pith_short_12","alias_value":"ICKJN2TPU3FH","created_at":"2026-05-18T12:27:09Z"},{"alias_kind":"pith_short_16","alias_value":"ICKJN2TPU3FH7Y4K","created_at":"2026-05-18T12:27:09Z"},{"alias_kind":"pith_short_8","alias_value":"ICKJN2TP","created_at":"2026-05-18T12:27:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:ICKJN2TPU3FH7Y4KTHPZIKK52U","target":"record","payload":{"canonical_record":{"source":{"id":"1201.4002","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-01-19T10:06:29Z","cross_cats_sorted":["cs.LG","math.OC"],"title_canon_sha256":"1521a0498fcc00d097754580e060e49e7d4198e1a146dd161baae018a0d9e5e6","abstract_canon_sha256":"e83afb3b0f927cf1f5558d41e9f3d0f192a5a311a903885865808ee602e3f157"},"schema_version":"1.0"},"canonical_sha256":"409496ea6fa6ca7fe38a99df94295dd5026dbc22064623d93b3f562615dbb149","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:04:16.185153Z","signature_b64":"9c11QItCLN+n71J8SWoYcFhMrWKHuPOP6HOzVO/3USFoi1YqRW4SUvDoxmD6rgKZ0LpYs9OsQtJGyE5GcrboDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"409496ea6fa6ca7fe38a99df94295dd5026dbc22064623d93b3f562615dbb149","last_reissued_at":"2026-05-18T04:04:16.184448Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:04:16.184448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1201.4002","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T04:04:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WPWoynnMXspwLYhoCH+oajexcvaq+d1fiJkvmgDeYnkaR403Qidx+6d0gnStzh8k/nmBQUZ2HojlK4X54Y0dCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T02:06:05.661924Z"},"content_sha256":"6432065ba5a159c267cceb5eb4f1ca5aa7ef65684071c3ce3586b84b774b8b52","schema_version":"1.0","event_id":"sha256:6432065ba5a159c267cceb5eb4f1ca5aa7ef65684071c3ce3586b84b774b8b52"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:ICKJN2TPU3FH7Y4KTHPZIKK52U","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.OC"],"primary_cat":"stat.ML","authors_text":"Apostolos Burnetas, Odysseas Kanavetas","submitted_at":"2012-01-19T10:06:29Z","abstract_excerpt":"We consider the problem of sequential sampling from a finite number of independent statistical populations to maximize the expected infinite horizon average outcome per period, under a constraint that the expected average sampling cost does not exceed an upper bound. The outcome distributions are not known. We construct a class of consistent adaptive policies, under which the average outcome converges with probability 1 to the true value under complete information for all distributions with finite means. We also compare the rate of convergence for various policies in this class using simulatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1201.4002","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T04:04:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JLsrOET1mu57bGsvRRy8cM2/M/GFbBpS/u+LejvwJjyyeDekO1wBG9zeQ8//r7SWQiRxr2Y5Rfjy0Jdt1nEGAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T02:06:05.662276Z"},"content_sha256":"ef7c1b36e8145b3d308148dcaef02805e3fd7598dd6f7fd8dac5db1bee03e19f","schema_version":"1.0","event_id":"sha256:ef7c1b36e8145b3d308148dcaef02805e3fd7598dd6f7fd8dac5db1bee03e19f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ICKJN2TPU3FH7Y4KTHPZIKK52U/bundle.json","state_url":"https://pith.science/pith/ICKJN2TPU3FH7Y4KTHPZIKK52U/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ICKJN2TPU3FH7Y4KTHPZIKK52U/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-23T02:06:05Z","links":{"resolver":"https://pith.science/pith/ICKJN2TPU3FH7Y4KTHPZIKK52U","bundle":"https://pith.science/pith/ICKJN2TPU3FH7Y4KTHPZIKK52U/bundle.json","state":"https://pith.science/pith/ICKJN2TPU3FH7Y4KTHPZIKK52U/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ICKJN2TPU3FH7Y4KTHPZIKK52U/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:ICKJN2TPU3FH7Y4KTHPZIKK52U","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e83afb3b0f927cf1f5558d41e9f3d0f192a5a311a903885865808ee602e3f157","cross_cats_sorted":["cs.LG","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-01-19T10:06:29Z","title_canon_sha256":"1521a0498fcc00d097754580e060e49e7d4198e1a146dd161baae018a0d9e5e6"},"schema_version":"1.0","source":{"id":"1201.4002","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1201.4002","created_at":"2026-05-18T04:04:16Z"},{"alias_kind":"arxiv_version","alias_value":"1201.4002v1","created_at":"2026-05-18T04:04:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1201.4002","created_at":"2026-05-18T04:04:16Z"},{"alias_kind":"pith_short_12","alias_value":"ICKJN2TPU3FH","created_at":"2026-05-18T12:27:09Z"},{"alias_kind":"pith_short_16","alias_value":"ICKJN2TPU3FH7Y4K","created_at":"2026-05-18T12:27:09Z"},{"alias_kind":"pith_short_8","alias_value":"ICKJN2TP","created_at":"2026-05-18T12:27:09Z"}],"graph_snapshots":[{"event_id":"sha256:ef7c1b36e8145b3d308148dcaef02805e3fd7598dd6f7fd8dac5db1bee03e19f","target":"graph","created_at":"2026-05-18T04:04:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"We consider the problem of sequential sampling from a finite number of independent statistical populations to maximize the expected infinite horizon average outcome per period, under a constraint that the expected average sampling cost does not exceed an upper bound. The outcome distributions are not known. We construct a class of consistent adaptive policies, under which the average outcome converges with probability 1 to the true value under complete information for all distributions with finite means. We also compare the rate of convergence for various policies in this class using simulatio","authors_text":"Apostolos Burnetas, Odysseas Kanavetas","cross_cats":["cs.LG","math.OC"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-01-19T10:06:29Z","title":"Adaptive Policies for Sequential Sampling under Incomplete Information and a Cost Constraint"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1201.4002","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6432065ba5a159c267cceb5eb4f1ca5aa7ef65684071c3ce3586b84b774b8b52","target":"record","created_at":"2026-05-18T04:04:16Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e83afb3b0f927cf1f5558d41e9f3d0f192a5a311a903885865808ee602e3f157","cross_cats_sorted":["cs.LG","math.OC"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2012-01-19T10:06:29Z","title_canon_sha256":"1521a0498fcc00d097754580e060e49e7d4198e1a146dd161baae018a0d9e5e6"},"schema_version":"1.0","source":{"id":"1201.4002","kind":"arxiv","version":1}},"canonical_sha256":"409496ea6fa6ca7fe38a99df94295dd5026dbc22064623d93b3f562615dbb149","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"409496ea6fa6ca7fe38a99df94295dd5026dbc22064623d93b3f562615dbb149","first_computed_at":"2026-05-18T04:04:16.184448Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:04:16.184448Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9c11QItCLN+n71J8SWoYcFhMrWKHuPOP6HOzVO/3USFoi1YqRW4SUvDoxmD6rgKZ0LpYs9OsQtJGyE5GcrboDw==","signature_status":"signed_v1","signed_at":"2026-05-18T04:04:16.185153Z","signed_message":"canonical_sha256_bytes"},"source_id":"1201.4002","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6432065ba5a159c267cceb5eb4f1ca5aa7ef65684071c3ce3586b84b774b8b52","sha256:ef7c1b36e8145b3d308148dcaef02805e3fd7598dd6f7fd8dac5db1bee03e19f"],"state_sha256":"51049c756722523154e7f4ee9bad0c490befb7d5204028eb3b6cbc8ca306afd6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nJvxLkOsRH3c4P/DFfNGwERq4oa8xLraM3IDNFY1EsrXc3IhN41jgHOSeh0ctEbBdoviXWJMfUyCKYrhLvzSDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-23T02:06:05.664432Z","bundle_sha256":"d58e52c20a4fc12eaee96f919f5877206390a10d36a9b5687e73fc896926f168"}}