{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:K46LEVJLVHHQFR6SBWA2HAQG75","short_pith_number":"pith:K46LEVJL","canonical_record":{"source":{"id":"1310.1267","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2013-10-04T13:47:41Z","cross_cats_sorted":[],"title_canon_sha256":"fac81817776446d12a30383c47f91176f7f78bbf26d213ef72c3ad68108e6228","abstract_canon_sha256":"d7d7c86ee98815b214a1b26688195278f8492ba636e9f6830c2910033b987c58"},"schema_version":"1.0"},"canonical_sha256":"573cb2552ba9cf02c7d20d81a38206ff59e81acd2d3020532e5f4aafe58db92f","source":{"kind":"arxiv","id":"1310.1267","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1310.1267","created_at":"2026-05-18T01:47:15Z"},{"alias_kind":"arxiv_version","alias_value":"1310.1267v1","created_at":"2026-05-18T01:47:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1310.1267","created_at":"2026-05-18T01:47:15Z"},{"alias_kind":"pith_short_12","alias_value":"K46LEVJLVHHQ","created_at":"2026-05-18T12:27:49Z"},{"alias_kind":"pith_short_16","alias_value":"K46LEVJLVHHQFR6S","created_at":"2026-05-18T12:27:49Z"},{"alias_kind":"pith_short_8","alias_value":"K46LEVJL","created_at":"2026-05-18T12:27:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:K46LEVJLVHHQFR6SBWA2HAQG75","target":"record","payload":{"canonical_record":{"source":{"id":"1310.1267","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2013-10-04T13:47:41Z","cross_cats_sorted":[],"title_canon_sha256":"fac81817776446d12a30383c47f91176f7f78bbf26d213ef72c3ad68108e6228","abstract_canon_sha256":"d7d7c86ee98815b214a1b26688195278f8492ba636e9f6830c2910033b987c58"},"schema_version":"1.0"},"canonical_sha256":"573cb2552ba9cf02c7d20d81a38206ff59e81acd2d3020532e5f4aafe58db92f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:47:15.251855Z","signature_b64":"+qCHlgazq27euS8EtkpYJm7Rj+6V3mVHQ8j5/HHZojErsJZUcloMy7tkIRCfGO2p5IfN0egT4OV2rpJYkCXUBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"573cb2552ba9cf02c7d20d81a38206ff59e81acd2d3020532e5f4aafe58db92f","last_reissued_at":"2026-05-18T01:47:15.251097Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:47:15.251097Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1310.1267","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":"2026-05-18T01:47:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jcejWBzHXoj2cP+kkEDo/v1sUP8rqeEM7olD6a36Wu4g5L2Uum9aRJ4lI4rsmiF8RSvhrNNKbAeQIL9IauvMAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T20:27:52.260192Z"},"content_sha256":"4fa4d4a4459cbe491cf81d9b6410dfb623894ae4f0d30d2077721b10e4fdc654","schema_version":"1.0","event_id":"sha256:4fa4d4a4459cbe491cf81d9b6410dfb623894ae4f0d30d2077721b10e4fdc654"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:K46LEVJLVHHQFR6SBWA2HAQG75","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Monte Carlo fixed-lag smoothing in state-space models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Anne Cuzol, Etienne M\\'emin","submitted_at":"2013-10-04T13:47:41Z","abstract_excerpt":"This paper presents an algorithm for Monte Carlo fixed-lag smoothing in state-space models defined by a diffusion process observed through noisy discrete-time measurements. Based on a particles approximation of the filtering and smoothing distributions, the method relies on a simulation technique of conditioned diffusions. The proposed sequential smoother can be applied to general non linear and multidimensional models, like the ones used in environmental applications. The smoothing of a turbulent flow in a high-dimensional context is given as a practical example."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1310.1267","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":"2026-05-18T01:47:15Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q3J77/IOplxMqJGjceo6/m98fhiFhnWkEcr9cBzdLrjYVIb3mVHlZ9jp5/N+kU7dXEy2OKKd1v0dbOADNjhnBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T20:27:52.260819Z"},"content_sha256":"49475b902e8a568f34b494d5daa7e4bc17d76e72bfbb2ade4e09c0fd53b8e258","schema_version":"1.0","event_id":"sha256:49475b902e8a568f34b494d5daa7e4bc17d76e72bfbb2ade4e09c0fd53b8e258"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/K46LEVJLVHHQFR6SBWA2HAQG75/bundle.json","state_url":"https://pith.science/pith/K46LEVJLVHHQFR6SBWA2HAQG75/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/K46LEVJLVHHQFR6SBWA2HAQG75/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":"2026-06-24T20:27:52Z","links":{"resolver":"https://pith.science/pith/K46LEVJLVHHQFR6SBWA2HAQG75","bundle":"https://pith.science/pith/K46LEVJLVHHQFR6SBWA2HAQG75/bundle.json","state":"https://pith.science/pith/K46LEVJLVHHQFR6SBWA2HAQG75/state.json","well_known_bundle":"https://pith.science/.well-known/pith/K46LEVJLVHHQFR6SBWA2HAQG75/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:K46LEVJLVHHQFR6SBWA2HAQG75","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":"d7d7c86ee98815b214a1b26688195278f8492ba636e9f6830c2910033b987c58","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2013-10-04T13:47:41Z","title_canon_sha256":"fac81817776446d12a30383c47f91176f7f78bbf26d213ef72c3ad68108e6228"},"schema_version":"1.0","source":{"id":"1310.1267","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1310.1267","created_at":"2026-05-18T01:47:15Z"},{"alias_kind":"arxiv_version","alias_value":"1310.1267v1","created_at":"2026-05-18T01:47:15Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1310.1267","created_at":"2026-05-18T01:47:15Z"},{"alias_kind":"pith_short_12","alias_value":"K46LEVJLVHHQ","created_at":"2026-05-18T12:27:49Z"},{"alias_kind":"pith_short_16","alias_value":"K46LEVJLVHHQFR6S","created_at":"2026-05-18T12:27:49Z"},{"alias_kind":"pith_short_8","alias_value":"K46LEVJL","created_at":"2026-05-18T12:27:49Z"}],"graph_snapshots":[{"event_id":"sha256:49475b902e8a568f34b494d5daa7e4bc17d76e72bfbb2ade4e09c0fd53b8e258","target":"graph","created_at":"2026-05-18T01:47:15Z","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 presents an algorithm for Monte Carlo fixed-lag smoothing in state-space models defined by a diffusion process observed through noisy discrete-time measurements. Based on a particles approximation of the filtering and smoothing distributions, the method relies on a simulation technique of conditioned diffusions. The proposed sequential smoother can be applied to general non linear and multidimensional models, like the ones used in environmental applications. The smoothing of a turbulent flow in a high-dimensional context is given as a practical example.","authors_text":"Anne Cuzol, Etienne M\\'emin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2013-10-04T13:47:41Z","title":"Monte Carlo fixed-lag smoothing in state-space models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1310.1267","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":"sha256:4fa4d4a4459cbe491cf81d9b6410dfb623894ae4f0d30d2077721b10e4fdc654","target":"record","created_at":"2026-05-18T01:47:15Z","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":"d7d7c86ee98815b214a1b26688195278f8492ba636e9f6830c2910033b987c58","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2013-10-04T13:47:41Z","title_canon_sha256":"fac81817776446d12a30383c47f91176f7f78bbf26d213ef72c3ad68108e6228"},"schema_version":"1.0","source":{"id":"1310.1267","kind":"arxiv","version":1}},"canonical_sha256":"573cb2552ba9cf02c7d20d81a38206ff59e81acd2d3020532e5f4aafe58db92f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"573cb2552ba9cf02c7d20d81a38206ff59e81acd2d3020532e5f4aafe58db92f","first_computed_at":"2026-05-18T01:47:15.251097Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:47:15.251097Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"+qCHlgazq27euS8EtkpYJm7Rj+6V3mVHQ8j5/HHZojErsJZUcloMy7tkIRCfGO2p5IfN0egT4OV2rpJYkCXUBA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:47:15.251855Z","signed_message":"canonical_sha256_bytes"},"source_id":"1310.1267","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4fa4d4a4459cbe491cf81d9b6410dfb623894ae4f0d30d2077721b10e4fdc654","sha256:49475b902e8a568f34b494d5daa7e4bc17d76e72bfbb2ade4e09c0fd53b8e258"],"state_sha256":"366aafa0603b0c137536991bd6f8dff3190b431c2dbb08a0dab294f590c7851b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4Zz/PLklbsIi/7oG6F7QArN7Et3NBtpvuFd/n4YAjSX++sCtIUqf4L9jwz2Gbd2HvezPwroJbEa8nBSK0Nr+AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-24T20:27:52.262631Z","bundle_sha256":"0143ea5cf9071e9d647acf922af7815b934b6b490ca352c0d481852f30b2b063"}}