{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:GTUJG3TD57IALADO3ZNP25EAY6","short_pith_number":"pith:GTUJG3TD","canonical_record":{"source":{"id":"1502.06045","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-02-21T00:29:53Z","cross_cats_sorted":[],"title_canon_sha256":"949f7bf18a6179c9af4e4ec0811bef6c103f2d997b2a192ce334e34fb4cf0359","abstract_canon_sha256":"4eb3872fb8d27d239a0f8d9fa2819bb3e318c333f11af9e5f05814d1088ce4af"},"schema_version":"1.0"},"canonical_sha256":"34e8936e63efd005806ede5afd7480c7ad3c8967685e1c7a296162dae47bd601","source":{"kind":"arxiv","id":"1502.06045","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.06045","created_at":"2026-05-18T02:26:38Z"},{"alias_kind":"arxiv_version","alias_value":"1502.06045v1","created_at":"2026-05-18T02:26:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.06045","created_at":"2026-05-18T02:26:38Z"},{"alias_kind":"pith_short_12","alias_value":"GTUJG3TD57IA","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_16","alias_value":"GTUJG3TD57IALADO","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_8","alias_value":"GTUJG3TD","created_at":"2026-05-18T12:29:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:GTUJG3TD57IALADO3ZNP25EAY6","target":"record","payload":{"canonical_record":{"source":{"id":"1502.06045","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-02-21T00:29:53Z","cross_cats_sorted":[],"title_canon_sha256":"949f7bf18a6179c9af4e4ec0811bef6c103f2d997b2a192ce334e34fb4cf0359","abstract_canon_sha256":"4eb3872fb8d27d239a0f8d9fa2819bb3e318c333f11af9e5f05814d1088ce4af"},"schema_version":"1.0"},"canonical_sha256":"34e8936e63efd005806ede5afd7480c7ad3c8967685e1c7a296162dae47bd601","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:26:38.215163Z","signature_b64":"42xt/cr/9uVjl3zVkIeOMfUPpNgkrSWvypO0VYi5tpXyRDGJWt5X/IUQHwF/ufkjnMO+QjDDhEfuGcJMaTV4Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34e8936e63efd005806ede5afd7480c7ad3c8967685e1c7a296162dae47bd601","last_reissued_at":"2026-05-18T02:26:38.214732Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:26:38.214732Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1502.06045","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":"2026-05-18T02:26:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5BJ9SydPBe/tCM6X6xufNJKUjj+NBrggKgvdQ+2jY3E9Gp60mdwjZ8HA7+AXx8jjqr81ksrIVC7KgqLDIqSZDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T08:13:47.429362Z"},"content_sha256":"eddd758aa065c6917d0a2c41e0ff1473c0cb02e410760b98f8c5b9877efa195b","schema_version":"1.0","event_id":"sha256:eddd758aa065c6917d0a2c41e0ff1473c0cb02e410760b98f8c5b9877efa195b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:GTUJG3TD57IALADO3ZNP25EAY6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Model specification via sequential coherence and backward induction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"P. Richard Hahn","submitted_at":"2015-02-21T00:29:53Z","abstract_excerpt":"This paper describes how to specify probability models for data analysis via a backward induction procedure. The new approach yields coherent, prior-free uncertainty assessment. After presenting some intuition-building examples, the new approach is applied to a kernel density estimator, which leads to a novel method for computing point-wise credible intervals in nonparametric density estimation. The new approach has two additional advantages; 1) the posterior mean density can be accurately approximated without resorting to Monte Carlo simulation and 2) concentration bounds are easily establish"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.06045","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":"2026-05-18T02:26:38Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8K04L7jBBlTV4cg0ujFdfHm/RZszzXdwlW69O/0XB4Yw2k09sKVO0JRaVANnQWmBfi9Ujs5phrQPEovaP9SyDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T08:13:47.430011Z"},"content_sha256":"5f864ac21eeecfab7eefed7d0510c6532b2429fc946581d73060aeb66f4e552f","schema_version":"1.0","event_id":"sha256:5f864ac21eeecfab7eefed7d0510c6532b2429fc946581d73060aeb66f4e552f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GTUJG3TD57IALADO3ZNP25EAY6/bundle.json","state_url":"https://pith.science/pith/GTUJG3TD57IALADO3ZNP25EAY6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GTUJG3TD57IALADO3ZNP25EAY6/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":"2026-05-27T08:13:47Z","links":{"resolver":"https://pith.science/pith/GTUJG3TD57IALADO3ZNP25EAY6","bundle":"https://pith.science/pith/GTUJG3TD57IALADO3ZNP25EAY6/bundle.json","state":"https://pith.science/pith/GTUJG3TD57IALADO3ZNP25EAY6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GTUJG3TD57IALADO3ZNP25EAY6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:GTUJG3TD57IALADO3ZNP25EAY6","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":"4eb3872fb8d27d239a0f8d9fa2819bb3e318c333f11af9e5f05814d1088ce4af","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-02-21T00:29:53Z","title_canon_sha256":"949f7bf18a6179c9af4e4ec0811bef6c103f2d997b2a192ce334e34fb4cf0359"},"schema_version":"1.0","source":{"id":"1502.06045","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.06045","created_at":"2026-05-18T02:26:38Z"},{"alias_kind":"arxiv_version","alias_value":"1502.06045v1","created_at":"2026-05-18T02:26:38Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.06045","created_at":"2026-05-18T02:26:38Z"},{"alias_kind":"pith_short_12","alias_value":"GTUJG3TD57IA","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_16","alias_value":"GTUJG3TD57IALADO","created_at":"2026-05-18T12:29:22Z"},{"alias_kind":"pith_short_8","alias_value":"GTUJG3TD","created_at":"2026-05-18T12:29:22Z"}],"graph_snapshots":[{"event_id":"sha256:5f864ac21eeecfab7eefed7d0510c6532b2429fc946581d73060aeb66f4e552f","target":"graph","created_at":"2026-05-18T02:26:38Z","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":"This paper describes how to specify probability models for data analysis via a backward induction procedure. The new approach yields coherent, prior-free uncertainty assessment. After presenting some intuition-building examples, the new approach is applied to a kernel density estimator, which leads to a novel method for computing point-wise credible intervals in nonparametric density estimation. The new approach has two additional advantages; 1) the posterior mean density can be accurately approximated without resorting to Monte Carlo simulation and 2) concentration bounds are easily establish","authors_text":"P. Richard Hahn","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-02-21T00:29:53Z","title":"Model specification via sequential coherence and backward induction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.06045","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:eddd758aa065c6917d0a2c41e0ff1473c0cb02e410760b98f8c5b9877efa195b","target":"record","created_at":"2026-05-18T02:26:38Z","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":"4eb3872fb8d27d239a0f8d9fa2819bb3e318c333f11af9e5f05814d1088ce4af","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-02-21T00:29:53Z","title_canon_sha256":"949f7bf18a6179c9af4e4ec0811bef6c103f2d997b2a192ce334e34fb4cf0359"},"schema_version":"1.0","source":{"id":"1502.06045","kind":"arxiv","version":1}},"canonical_sha256":"34e8936e63efd005806ede5afd7480c7ad3c8967685e1c7a296162dae47bd601","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"34e8936e63efd005806ede5afd7480c7ad3c8967685e1c7a296162dae47bd601","first_computed_at":"2026-05-18T02:26:38.214732Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:26:38.214732Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"42xt/cr/9uVjl3zVkIeOMfUPpNgkrSWvypO0VYi5tpXyRDGJWt5X/IUQHwF/ufkjnMO+QjDDhEfuGcJMaTV4Ag==","signature_status":"signed_v1","signed_at":"2026-05-18T02:26:38.215163Z","signed_message":"canonical_sha256_bytes"},"source_id":"1502.06045","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eddd758aa065c6917d0a2c41e0ff1473c0cb02e410760b98f8c5b9877efa195b","sha256:5f864ac21eeecfab7eefed7d0510c6532b2429fc946581d73060aeb66f4e552f"],"state_sha256":"507451cf508a43b85629e7e9e3621f7f5e4893fa556c0a5c7463950a3fbdfff7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0GMDVI5FDNPBKlfzg7CSa60Gtk/FJ2bsknUwbIw6IIsA9RrW/PzP38Rizh5EpIfIB2iqo8no9UM95KwdJpHeAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T08:13:47.435917Z","bundle_sha256":"242526481bf36cba082f1c32f65cbf728ae301b9464a8b9ee51fe0ebd400caf8"}}