{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:BT54AK3ZK27MDET2BEPYD7PWOM","short_pith_number":"pith:BT54AK3Z","canonical_record":{"source":{"id":"1409.1787","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-09-05T13:35:52Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"e7fdc9ac4a42390bb8d087fc37dd6d03c0b2daa197f03e203d692f395911475b","abstract_canon_sha256":"d2b110c345e4e473b94f36edf85e48bea20c368691ebaf3d6f3a8681c323ce1a"},"schema_version":"1.0"},"canonical_sha256":"0cfbc02b7956bec1927a091f81fdf6732870401455c1eefb2f6121215f1974f9","source":{"kind":"arxiv","id":"1409.1787","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.1787","created_at":"2026-05-18T02:17:01Z"},{"alias_kind":"arxiv_version","alias_value":"1409.1787v2","created_at":"2026-05-18T02:17:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.1787","created_at":"2026-05-18T02:17:01Z"},{"alias_kind":"pith_short_12","alias_value":"BT54AK3ZK27M","created_at":"2026-05-18T12:28:22Z"},{"alias_kind":"pith_short_16","alias_value":"BT54AK3ZK27MDET2","created_at":"2026-05-18T12:28:22Z"},{"alias_kind":"pith_short_8","alias_value":"BT54AK3Z","created_at":"2026-05-18T12:28:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:BT54AK3ZK27MDET2BEPYD7PWOM","target":"record","payload":{"canonical_record":{"source":{"id":"1409.1787","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-09-05T13:35:52Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"e7fdc9ac4a42390bb8d087fc37dd6d03c0b2daa197f03e203d692f395911475b","abstract_canon_sha256":"d2b110c345e4e473b94f36edf85e48bea20c368691ebaf3d6f3a8681c323ce1a"},"schema_version":"1.0"},"canonical_sha256":"0cfbc02b7956bec1927a091f81fdf6732870401455c1eefb2f6121215f1974f9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:17:01.176465Z","signature_b64":"LX4u1hbpj+D62lknSqv+Ie3QXzpdR4TKbJQgfrmjKW0AVOfN1eK0l+ECHz9CZCt1K0PHpUqli7h7mGDvTkLcCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0cfbc02b7956bec1927a091f81fdf6732870401455c1eefb2f6121215f1974f9","last_reissued_at":"2026-05-18T02:17:01.175879Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:17:01.175879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1409.1787","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":"2026-05-18T02:17:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JrHVYItUkq429VY0qs5kwmvQsyFFig2kboNOYdr3Tweap4tZfIbNMrYSA32pSbIxTGGFjdjcwCKiyvbIAr7DDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T02:21:20.907816Z"},"content_sha256":"6c5ef6275bbf574799e4511c6ecc0e67731f9c443b6a4d0d897d4b33948378b8","schema_version":"1.0","event_id":"sha256:6c5ef6275bbf574799e4511c6ecc0e67731f9c443b6a4d0d897d4b33948378b8"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:BT54AK3ZK27MDET2BEPYD7PWOM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A new framework for extracting coarse-grained models from time series with multiscale structure","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Grigorios A. Pavliotis, Sebastian Krumscheid, Serafim Kalliadasis","submitted_at":"2014-09-05T13:35:52Z","abstract_excerpt":"In many applications it is desirable to infer coarse-grained models from observational data. The observed process often corresponds only to a few selected degrees of freedom of a high-dimensional dynamical system with multiple time scales. In this work we consider the inference problem of identifying an appropriate coarse-grained model from a single time series of a multiscale system. It is known that estimators such as the maximum likelihood estimator or the quadratic variation of the path estimator can be strongly biased in this setting. Here we present a novel parametric inference methodolo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.1787","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":"2026-05-18T02:17:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"70ItObo1jNgRR88PjlNLIZYwLxGaxSRMBYdhPoRLL8GRhmGgfWgw5FOnAviqUWE9hvHl22r9Ex6vO8w3rFurCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T02:21:20.908158Z"},"content_sha256":"7be08f63dd961f24cdba59eafd62989536bee13e2a7269fe53b48d26a0ef32ab","schema_version":"1.0","event_id":"sha256:7be08f63dd961f24cdba59eafd62989536bee13e2a7269fe53b48d26a0ef32ab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BT54AK3ZK27MDET2BEPYD7PWOM/bundle.json","state_url":"https://pith.science/pith/BT54AK3ZK27MDET2BEPYD7PWOM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BT54AK3ZK27MDET2BEPYD7PWOM/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":"2026-07-04T02:21:20Z","links":{"resolver":"https://pith.science/pith/BT54AK3ZK27MDET2BEPYD7PWOM","bundle":"https://pith.science/pith/BT54AK3ZK27MDET2BEPYD7PWOM/bundle.json","state":"https://pith.science/pith/BT54AK3ZK27MDET2BEPYD7PWOM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BT54AK3ZK27MDET2BEPYD7PWOM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:BT54AK3ZK27MDET2BEPYD7PWOM","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":"d2b110c345e4e473b94f36edf85e48bea20c368691ebaf3d6f3a8681c323ce1a","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-09-05T13:35:52Z","title_canon_sha256":"e7fdc9ac4a42390bb8d087fc37dd6d03c0b2daa197f03e203d692f395911475b"},"schema_version":"1.0","source":{"id":"1409.1787","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1409.1787","created_at":"2026-05-18T02:17:01Z"},{"alias_kind":"arxiv_version","alias_value":"1409.1787v2","created_at":"2026-05-18T02:17:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1409.1787","created_at":"2026-05-18T02:17:01Z"},{"alias_kind":"pith_short_12","alias_value":"BT54AK3ZK27M","created_at":"2026-05-18T12:28:22Z"},{"alias_kind":"pith_short_16","alias_value":"BT54AK3ZK27MDET2","created_at":"2026-05-18T12:28:22Z"},{"alias_kind":"pith_short_8","alias_value":"BT54AK3Z","created_at":"2026-05-18T12:28:22Z"}],"graph_snapshots":[{"event_id":"sha256:7be08f63dd961f24cdba59eafd62989536bee13e2a7269fe53b48d26a0ef32ab","target":"graph","created_at":"2026-05-18T02:17:01Z","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":"In many applications it is desirable to infer coarse-grained models from observational data. The observed process often corresponds only to a few selected degrees of freedom of a high-dimensional dynamical system with multiple time scales. In this work we consider the inference problem of identifying an appropriate coarse-grained model from a single time series of a multiscale system. It is known that estimators such as the maximum likelihood estimator or the quadratic variation of the path estimator can be strongly biased in this setting. Here we present a novel parametric inference methodolo","authors_text":"Grigorios A. Pavliotis, Sebastian Krumscheid, Serafim Kalliadasis","cross_cats":["stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-09-05T13:35:52Z","title":"A new framework for extracting coarse-grained models from time series with multiscale structure"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1409.1787","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":"sha256:6c5ef6275bbf574799e4511c6ecc0e67731f9c443b6a4d0d897d4b33948378b8","target":"record","created_at":"2026-05-18T02:17:01Z","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":"d2b110c345e4e473b94f36edf85e48bea20c368691ebaf3d6f3a8681c323ce1a","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2014-09-05T13:35:52Z","title_canon_sha256":"e7fdc9ac4a42390bb8d087fc37dd6d03c0b2daa197f03e203d692f395911475b"},"schema_version":"1.0","source":{"id":"1409.1787","kind":"arxiv","version":2}},"canonical_sha256":"0cfbc02b7956bec1927a091f81fdf6732870401455c1eefb2f6121215f1974f9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0cfbc02b7956bec1927a091f81fdf6732870401455c1eefb2f6121215f1974f9","first_computed_at":"2026-05-18T02:17:01.175879Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:17:01.175879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LX4u1hbpj+D62lknSqv+Ie3QXzpdR4TKbJQgfrmjKW0AVOfN1eK0l+ECHz9CZCt1K0PHpUqli7h7mGDvTkLcCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:17:01.176465Z","signed_message":"canonical_sha256_bytes"},"source_id":"1409.1787","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6c5ef6275bbf574799e4511c6ecc0e67731f9c443b6a4d0d897d4b33948378b8","sha256:7be08f63dd961f24cdba59eafd62989536bee13e2a7269fe53b48d26a0ef32ab"],"state_sha256":"7250f61f9dd2cf1fc314253b39d2f4ba5fe188b32c5534568e606b719770c844"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Py68CjZFYFRQAmEDOrJ3mQiaPrxR1pW2IY4scoc43NZqRKm7UsrGjA320WUOPyjA2nKDiFztu3ruwlm3wW+7BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T02:21:20.909910Z","bundle_sha256":"909bf9b6770191628be54b451653beab98ca1cf148ff08674e948b10ab94ad6b"}}