{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:MPUM4VED7UUEI3Q5IDHE5BB3EA","short_pith_number":"pith:MPUM4VED","canonical_record":{"source":{"id":"1211.3760","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-11-15T21:45:15Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ba6faa247da612a4b34588740f53ab099981531149670884cb6c47dbfcc799d8","abstract_canon_sha256":"3efc6154205465845f317cd1837307d931a8b69ec13236bcf6afdeda254af308"},"schema_version":"1.0"},"canonical_sha256":"63e8ce5483fd28446e1d40ce4e843b203b1c0b2a366ec65c13b0c7770c61a2fd","source":{"kind":"arxiv","id":"1211.3760","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1211.3760","created_at":"2026-05-18T01:36:23Z"},{"alias_kind":"arxiv_version","alias_value":"1211.3760v2","created_at":"2026-05-18T01:36:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1211.3760","created_at":"2026-05-18T01:36:23Z"},{"alias_kind":"pith_short_12","alias_value":"MPUM4VED7UUE","created_at":"2026-05-18T12:27:14Z"},{"alias_kind":"pith_short_16","alias_value":"MPUM4VED7UUEI3Q5","created_at":"2026-05-18T12:27:14Z"},{"alias_kind":"pith_short_8","alias_value":"MPUM4VED","created_at":"2026-05-18T12:27:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:MPUM4VED7UUEI3Q5IDHE5BB3EA","target":"record","payload":{"canonical_record":{"source":{"id":"1211.3760","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-11-15T21:45:15Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"ba6faa247da612a4b34588740f53ab099981531149670884cb6c47dbfcc799d8","abstract_canon_sha256":"3efc6154205465845f317cd1837307d931a8b69ec13236bcf6afdeda254af308"},"schema_version":"1.0"},"canonical_sha256":"63e8ce5483fd28446e1d40ce4e843b203b1c0b2a366ec65c13b0c7770c61a2fd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:36:23.021780Z","signature_b64":"U7cJ1tgs8i6kiFut30+RqmI4j9VjtfsA43JN3BZy8T0sPxQ33hsLavKDyK4znspjUhxVLN5Me5eqkt9i0k5OCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"63e8ce5483fd28446e1d40ce4e843b203b1c0b2a366ec65c13b0c7770c61a2fd","last_reissued_at":"2026-05-18T01:36:23.021259Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:36:23.021259Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1211.3760","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:36:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NYw1E97ZLj9UwMNP5C9PLKScA38vcrZtg5fXfzx/3H+SaU2vu4uzBvuAFOkCUPEio9epmgYLpF2UnPnAIaxmCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T12:00:01.807247Z"},"content_sha256":"85a7b1f55db3d2d7444be404edcbc78345bd18e3f339f13b2745f23f5356026f","schema_version":"1.0","event_id":"sha256:85a7b1f55db3d2d7444be404edcbc78345bd18e3f339f13b2745f23f5356026f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:MPUM4VED7UUEI3Q5IDHE5BB3EA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Cosma Rohilla Shalizi, Georg M. Goerg","submitted_at":"2012-11-15T21:45:15Z","abstract_excerpt":"We introduce 'mixed LICORS', an algorithm for learning nonlinear, high-dimensional dynamics from spatio-temporal data, suitable for both prediction and simulation. Mixed LICORS extends the recent LICORS algorithm (Goerg and Shalizi, 2012) from hard clustering of predictive distributions to a non-parametric, EM-like soft clustering. This retains the asymptotic predictive optimality of LICORS, but, as we show in simulations, greatly improves out-of-sample forecasts with limited data. The new method is implemented in the publicly-available R package \"LICORS\" (http://cran.r-project.org/web/package"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1211.3760","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:36:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c5TduX4HR4l+7alwUdrgiC6Jo2WIUXSm5xRPnpl9XlOC4hVOfjEkLqUgWa0lIG0g4kRld65dg7oAtU78Sl8FAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T12:00:01.807611Z"},"content_sha256":"079ca645d87c062ce1999c85950eb85ed9ca1c2cc1e9981a2669f68c98eb228f","schema_version":"1.0","event_id":"sha256:079ca645d87c062ce1999c85950eb85ed9ca1c2cc1e9981a2669f68c98eb228f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MPUM4VED7UUEI3Q5IDHE5BB3EA/bundle.json","state_url":"https://pith.science/pith/MPUM4VED7UUEI3Q5IDHE5BB3EA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MPUM4VED7UUEI3Q5IDHE5BB3EA/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":"2026-06-24T12:00:01Z","links":{"resolver":"https://pith.science/pith/MPUM4VED7UUEI3Q5IDHE5BB3EA","bundle":"https://pith.science/pith/MPUM4VED7UUEI3Q5IDHE5BB3EA/bundle.json","state":"https://pith.science/pith/MPUM4VED7UUEI3Q5IDHE5BB3EA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MPUM4VED7UUEI3Q5IDHE5BB3EA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:MPUM4VED7UUEI3Q5IDHE5BB3EA","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":"3efc6154205465845f317cd1837307d931a8b69ec13236bcf6afdeda254af308","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-11-15T21:45:15Z","title_canon_sha256":"ba6faa247da612a4b34588740f53ab099981531149670884cb6c47dbfcc799d8"},"schema_version":"1.0","source":{"id":"1211.3760","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1211.3760","created_at":"2026-05-18T01:36:23Z"},{"alias_kind":"arxiv_version","alias_value":"1211.3760v2","created_at":"2026-05-18T01:36:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1211.3760","created_at":"2026-05-18T01:36:23Z"},{"alias_kind":"pith_short_12","alias_value":"MPUM4VED7UUE","created_at":"2026-05-18T12:27:14Z"},{"alias_kind":"pith_short_16","alias_value":"MPUM4VED7UUEI3Q5","created_at":"2026-05-18T12:27:14Z"},{"alias_kind":"pith_short_8","alias_value":"MPUM4VED","created_at":"2026-05-18T12:27:14Z"}],"graph_snapshots":[{"event_id":"sha256:079ca645d87c062ce1999c85950eb85ed9ca1c2cc1e9981a2669f68c98eb228f","target":"graph","created_at":"2026-05-18T01:36:23Z","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 introduce 'mixed LICORS', an algorithm for learning nonlinear, high-dimensional dynamics from spatio-temporal data, suitable for both prediction and simulation. Mixed LICORS extends the recent LICORS algorithm (Goerg and Shalizi, 2012) from hard clustering of predictive distributions to a non-parametric, EM-like soft clustering. This retains the asymptotic predictive optimality of LICORS, but, as we show in simulations, greatly improves out-of-sample forecasts with limited data. The new method is implemented in the publicly-available R package \"LICORS\" (http://cran.r-project.org/web/package","authors_text":"Cosma Rohilla Shalizi, Georg M. Goerg","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-11-15T21:45:15Z","title":"Mixed LICORS: A Nonparametric Algorithm for Predictive State Reconstruction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1211.3760","kind":"arxiv","version":2},"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":"sha256:85a7b1f55db3d2d7444be404edcbc78345bd18e3f339f13b2745f23f5356026f","target":"record","created_at":"2026-05-18T01:36:23Z","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":"3efc6154205465845f317cd1837307d931a8b69ec13236bcf6afdeda254af308","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-11-15T21:45:15Z","title_canon_sha256":"ba6faa247da612a4b34588740f53ab099981531149670884cb6c47dbfcc799d8"},"schema_version":"1.0","source":{"id":"1211.3760","kind":"arxiv","version":2}},"canonical_sha256":"63e8ce5483fd28446e1d40ce4e843b203b1c0b2a366ec65c13b0c7770c61a2fd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"63e8ce5483fd28446e1d40ce4e843b203b1c0b2a366ec65c13b0c7770c61a2fd","first_computed_at":"2026-05-18T01:36:23.021259Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:36:23.021259Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"U7cJ1tgs8i6kiFut30+RqmI4j9VjtfsA43JN3BZy8T0sPxQ33hsLavKDyK4znspjUhxVLN5Me5eqkt9i0k5OCw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:36:23.021780Z","signed_message":"canonical_sha256_bytes"},"source_id":"1211.3760","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:85a7b1f55db3d2d7444be404edcbc78345bd18e3f339f13b2745f23f5356026f","sha256:079ca645d87c062ce1999c85950eb85ed9ca1c2cc1e9981a2669f68c98eb228f"],"state_sha256":"ceaa0a291acb1c666d7081faced96af6fa2ccd7879ce950d1a4e0e8616511759"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kIbUQ6EbPG4ebv67hC4CcLUO3+sg28E41+Jllr4qfsHLeFhZncmIqhzbBxpeEU/dvZqRSPQGqNBIZHGUNWKoAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-24T12:00:01.809525Z","bundle_sha256":"d13063f5a0210012fce7e07cd6b551941edfd684398bf43549c843d7f06e85a0"}}