{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:NB25EPHSH7GCWQUQPXGY4A5DZE","short_pith_number":"pith:NB25EPHS","canonical_record":{"source":{"id":"2406.18066","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-26T04:51:14Z","cross_cats_sorted":["math.DS"],"title_canon_sha256":"dbae43c3f8237d02d9d98fbc52c71c868ba044fd3c1aaceac92e6931e7bc8606","abstract_canon_sha256":"6531ad4c50e817ebcfdf549b6e3f05a8fa68a572ecba33ea25b6f65feaf320c0"},"schema_version":"1.0"},"canonical_sha256":"6875d23cf23fcc2b42907dcd8e03a3c903fe4d5f120379e531156c828f811529","source":{"kind":"arxiv","id":"2406.18066","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.18066","created_at":"2026-07-05T10:37:27Z"},{"alias_kind":"arxiv_version","alias_value":"2406.18066v3","created_at":"2026-07-05T10:37:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.18066","created_at":"2026-07-05T10:37:27Z"},{"alias_kind":"pith_short_12","alias_value":"NB25EPHSH7GC","created_at":"2026-07-05T10:37:27Z"},{"alias_kind":"pith_short_16","alias_value":"NB25EPHSH7GCWQUQ","created_at":"2026-07-05T10:37:27Z"},{"alias_kind":"pith_short_8","alias_value":"NB25EPHS","created_at":"2026-07-05T10:37:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:NB25EPHSH7GCWQUQPXGY4A5DZE","target":"record","payload":{"canonical_record":{"source":{"id":"2406.18066","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-26T04:51:14Z","cross_cats_sorted":["math.DS"],"title_canon_sha256":"dbae43c3f8237d02d9d98fbc52c71c868ba044fd3c1aaceac92e6931e7bc8606","abstract_canon_sha256":"6531ad4c50e817ebcfdf549b6e3f05a8fa68a572ecba33ea25b6f65feaf320c0"},"schema_version":"1.0"},"canonical_sha256":"6875d23cf23fcc2b42907dcd8e03a3c903fe4d5f120379e531156c828f811529","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:37:27.815590Z","signature_b64":"A8cwNohIyWMiXwshpy4lE+Fh+lhKHvcMCI0mn63SrMm2mX/yCvqETbr8b2wRE6SOeP2hqht5GTdodAPYUYi4BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6875d23cf23fcc2b42907dcd8e03a3c903fe4d5f120379e531156c828f811529","last_reissued_at":"2026-07-05T10:37:27.814978Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:37:27.814978Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.18066","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:37:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qDex2T2dO1+2dd2xXdh5K76lHaGEob3Dxxfl5khw8sDmXvqVOFckh7ooQbC07gvVYzfWhA4TSLAEEEgE9i5gAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T08:45:04.062859Z"},"content_sha256":"d5bcd2fdc3cd9bff0fe8931dfdb14ae7cf6c941d876c3702a92370c3882833a4","schema_version":"1.0","event_id":"sha256:d5bcd2fdc3cd9bff0fe8931dfdb14ae7cf6c941d876c3702a92370c3882833a4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:NB25EPHSH7GCWQUQPXGY4A5DZE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Optimal Filters Using Variational Inference","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["math.DS"],"primary_cat":"cs.LG","authors_text":"Andrew Stuart, Enoch Luk, Eviatar Bach, Ricardo Baptista","submitted_at":"2024-06-26T04:51:14Z","abstract_excerpt":"Filtering - the task of estimating the conditional distribution for states of a dynamical system given partial and noisy observations - is important in many areas of science and engineering, including weather and climate prediction. However, the filtering distribution is generally intractable to obtain for high-dimensional, nonlinear systems. Filters used in practice, such as the ensemble Kalman filter (EnKF), provide biased probabilistic estimates for nonlinear systems and have numerous tuning parameters. Here, we present a framework for learning a parameterized analysis map - the transformat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.18066","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2406.18066/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T10:37:27Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+oM8uxSNkqRGqPo0AG1WbX+cdnsYXA855UeN5nZ21Uk5LnsYa+E+Vevq3xrZgEoWitiwiZt0AQUWl9m8yYg2DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T08:45:04.063638Z"},"content_sha256":"e6ea0ce20d07842f412b73415555bb869848a0ff7111dc15da8378d83f284c2a","schema_version":"1.0","event_id":"sha256:e6ea0ce20d07842f412b73415555bb869848a0ff7111dc15da8378d83f284c2a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NB25EPHSH7GCWQUQPXGY4A5DZE/bundle.json","state_url":"https://pith.science/pith/NB25EPHSH7GCWQUQPXGY4A5DZE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NB25EPHSH7GCWQUQPXGY4A5DZE/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":"2026-08-18T08:45:04Z","links":{"resolver":"https://pith.science/pith/NB25EPHSH7GCWQUQPXGY4A5DZE","bundle":"https://pith.science/pith/NB25EPHSH7GCWQUQPXGY4A5DZE/bundle.json","state":"https://pith.science/pith/NB25EPHSH7GCWQUQPXGY4A5DZE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NB25EPHSH7GCWQUQPXGY4A5DZE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NB25EPHSH7GCWQUQPXGY4A5DZE","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":"6531ad4c50e817ebcfdf549b6e3f05a8fa68a572ecba33ea25b6f65feaf320c0","cross_cats_sorted":["math.DS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-26T04:51:14Z","title_canon_sha256":"dbae43c3f8237d02d9d98fbc52c71c868ba044fd3c1aaceac92e6931e7bc8606"},"schema_version":"1.0","source":{"id":"2406.18066","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.18066","created_at":"2026-07-05T10:37:27Z"},{"alias_kind":"arxiv_version","alias_value":"2406.18066v3","created_at":"2026-07-05T10:37:27Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.18066","created_at":"2026-07-05T10:37:27Z"},{"alias_kind":"pith_short_12","alias_value":"NB25EPHSH7GC","created_at":"2026-07-05T10:37:27Z"},{"alias_kind":"pith_short_16","alias_value":"NB25EPHSH7GCWQUQ","created_at":"2026-07-05T10:37:27Z"},{"alias_kind":"pith_short_8","alias_value":"NB25EPHS","created_at":"2026-07-05T10:37:27Z"}],"graph_snapshots":[{"event_id":"sha256:e6ea0ce20d07842f412b73415555bb869848a0ff7111dc15da8378d83f284c2a","target":"graph","created_at":"2026-07-05T10:37:27Z","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/2406.18066/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Filtering - the task of estimating the conditional distribution for states of a dynamical system given partial and noisy observations - is important in many areas of science and engineering, including weather and climate prediction. However, the filtering distribution is generally intractable to obtain for high-dimensional, nonlinear systems. Filters used in practice, such as the ensemble Kalman filter (EnKF), provide biased probabilistic estimates for nonlinear systems and have numerous tuning parameters. Here, we present a framework for learning a parameterized analysis map - the transformat","authors_text":"Andrew Stuart, Enoch Luk, Eviatar Bach, Ricardo Baptista","cross_cats":["math.DS"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-26T04:51:14Z","title":"Learning Optimal Filters Using Variational Inference"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.18066","kind":"arxiv","version":3},"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:d5bcd2fdc3cd9bff0fe8931dfdb14ae7cf6c941d876c3702a92370c3882833a4","target":"record","created_at":"2026-07-05T10:37:27Z","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":"6531ad4c50e817ebcfdf549b6e3f05a8fa68a572ecba33ea25b6f65feaf320c0","cross_cats_sorted":["math.DS"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-26T04:51:14Z","title_canon_sha256":"dbae43c3f8237d02d9d98fbc52c71c868ba044fd3c1aaceac92e6931e7bc8606"},"schema_version":"1.0","source":{"id":"2406.18066","kind":"arxiv","version":3}},"canonical_sha256":"6875d23cf23fcc2b42907dcd8e03a3c903fe4d5f120379e531156c828f811529","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6875d23cf23fcc2b42907dcd8e03a3c903fe4d5f120379e531156c828f811529","first_computed_at":"2026-07-05T10:37:27.814978Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:37:27.814978Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"A8cwNohIyWMiXwshpy4lE+Fh+lhKHvcMCI0mn63SrMm2mX/yCvqETbr8b2wRE6SOeP2hqht5GTdodAPYUYi4BA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:37:27.815590Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.18066","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d5bcd2fdc3cd9bff0fe8931dfdb14ae7cf6c941d876c3702a92370c3882833a4","sha256:e6ea0ce20d07842f412b73415555bb869848a0ff7111dc15da8378d83f284c2a"],"state_sha256":"1ac423608a222af67d0c047e2bea49caa03de801eecaa54faa4376fa20a305b0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lOVkgbjeHu/JL4Wfruj+HeEk0rSY9CXc9kQcdACVUJ11C3eFtXR2cA+bthBP7NOeB4opDZd/zk5LRs3/HDIuBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T08:45:04.071124Z","bundle_sha256":"3fdc33610d0617494c373c2d041a6d1ccdcf3b8c6304ce6b6239825c26d687c0"}}