{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:KO6PT4H4BU5BT3FQJ6K5EZQDCR","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":"6d57a58ebd0817ff410304c1858b77490657ed3d81b5aeb36597c293e2eb65a2","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2020-12-01T06:14:16Z","title_canon_sha256":"4698f792adbcd18129a8b90529049ab58b62948d5c3adc4101b6050a0713845c"},"schema_version":"1.0","source":{"id":"2012.01185","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2012.01185","created_at":"2026-07-05T01:56:36Z"},{"alias_kind":"arxiv_version","alias_value":"2012.01185v1","created_at":"2026-07-05T01:56:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2012.01185","created_at":"2026-07-05T01:56:36Z"},{"alias_kind":"pith_short_12","alias_value":"KO6PT4H4BU5B","created_at":"2026-07-05T01:56:36Z"},{"alias_kind":"pith_short_16","alias_value":"KO6PT4H4BU5BT3FQ","created_at":"2026-07-05T01:56:36Z"},{"alias_kind":"pith_short_8","alias_value":"KO6PT4H4","created_at":"2026-07-05T01:56:36Z"}],"graph_snapshots":[{"event_id":"sha256:c59e91f18dd8991778109253f765f94c5c8ae408b432a07fbba004ed24c73a03","target":"graph","created_at":"2026-07-05T01:56:36Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2012.01185/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present new algorithms and fast implementations to find efficient approximations for modelling stochastic processes. For many numerical computations it is essential to develop finite approximations for stochastic processes. While the goal is always to find a finite model, which represents a given knowledge about the real data process as accurate as possible, the ways of estimating the discrete approximating model may be quite different: (i) if the stochastic model is known as a solution of a stochastic differential equation, e.g., one may generate the scenario tree directly from the specifi","authors_text":"Alois Pichler, Georg Ch. Pflug, Kipngeno Benard Kirui","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2020-12-01T06:14:16Z","title":"New Algorithms And Fast Implementations To Approximate Stochastic Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2012.01185","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":"sha256:15a40da22c1a1f0996bed761c0b97e7a9fe6f6236987d08014d0d49c873cabee","target":"record","created_at":"2026-07-05T01:56:36Z","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":"6d57a58ebd0817ff410304c1858b77490657ed3d81b5aeb36597c293e2eb65a2","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.OC","submitted_at":"2020-12-01T06:14:16Z","title_canon_sha256":"4698f792adbcd18129a8b90529049ab58b62948d5c3adc4101b6050a0713845c"},"schema_version":"1.0","source":{"id":"2012.01185","kind":"arxiv","version":1}},"canonical_sha256":"53bcf9f0fc0d3a19ecb04f95d26603145581260abc0bd477ff0dd87ef76395d4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"53bcf9f0fc0d3a19ecb04f95d26603145581260abc0bd477ff0dd87ef76395d4","first_computed_at":"2026-07-05T01:56:36.573363Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:56:36.573363Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"j2Am2R/4WB0aWNitm5fZe7NvFG7zkvJSmnrw/0t/pctLD2He3PcYFkM439ZV9u5XiJW253kg+zYpxRODw7crAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:56:36.573780Z","signed_message":"canonical_sha256_bytes"},"source_id":"2012.01185","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:15a40da22c1a1f0996bed761c0b97e7a9fe6f6236987d08014d0d49c873cabee","sha256:c59e91f18dd8991778109253f765f94c5c8ae408b432a07fbba004ed24c73a03"],"state_sha256":"568bd39bc52e883e35c57d56ffc7dafa199b279710814da7349905d749afa191"}