{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:3XHYTNWOLUQ6PRS3QYQBSUVDFL","short_pith_number":"pith:3XHYTNWO","canonical_record":{"source":{"id":"1708.02340","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-08-08T00:45:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b0fd41e446ae1710b0c80b46b7f8e2b8074b5cad6495fbcfc92a0d21d414dce5","abstract_canon_sha256":"e6aaad8b434b4a665f46087abf9ab3bbbcbfbaa4c3637680417a603480094257"},"schema_version":"1.0"},"canonical_sha256":"ddcf89b6ce5d21e7c65b86201952a32afa2a1fbfa7b5b8ed54933fe99d3ef871","source":{"kind":"arxiv","id":"1708.02340","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.02340","created_at":"2026-05-17T23:44:22Z"},{"alias_kind":"arxiv_version","alias_value":"1708.02340v2","created_at":"2026-05-17T23:44:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.02340","created_at":"2026-05-17T23:44:22Z"},{"alias_kind":"pith_short_12","alias_value":"3XHYTNWOLUQ6","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"3XHYTNWOLUQ6PRS3","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"3XHYTNWO","created_at":"2026-05-18T12:30:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:3XHYTNWOLUQ6PRS3QYQBSUVDFL","target":"record","payload":{"canonical_record":{"source":{"id":"1708.02340","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-08-08T00:45:18Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b0fd41e446ae1710b0c80b46b7f8e2b8074b5cad6495fbcfc92a0d21d414dce5","abstract_canon_sha256":"e6aaad8b434b4a665f46087abf9ab3bbbcbfbaa4c3637680417a603480094257"},"schema_version":"1.0"},"canonical_sha256":"ddcf89b6ce5d21e7c65b86201952a32afa2a1fbfa7b5b8ed54933fe99d3ef871","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:22.998530Z","signature_b64":"dxrybrvgS2B1qTYfUkobTsWdH6McwgZrFnwUxoEgHbe92BGWjolVlM4kquCleTu82itHUBrlT34/uvLMSCt8Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ddcf89b6ce5d21e7c65b86201952a32afa2a1fbfa7b5b8ed54933fe99d3ef871","last_reissued_at":"2026-05-17T23:44:22.997878Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:22.997878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.02340","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":"2026-05-17T23:44:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"J2eI8bEfMRzk+YzVSAvrP46NhQTDqEBd5y2scFKUYx+YuXoO9wTO48EkH2fNUtkiuyfAHuGEuCGxjwNqwTu/AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T09:54:34.599912Z"},"content_sha256":"acd3660840c50d3db90df02a2248f0e43852d46d386bd63447ed0cdf155b961b","schema_version":"1.0","event_id":"sha256:acd3660840c50d3db90df02a2248f0e43852d46d386bd63447ed0cdf155b961b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:3XHYTNWOLUQ6PRS3QYQBSUVDFL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"EnLLVM: Ensemble Based Nonlinear Bayesian Filtering Using Linear Latent Variable Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.CO","authors_text":"Gabriel Terejanu, Xiao Lin","submitted_at":"2017-08-08T00:45:18Z","abstract_excerpt":"Real-time nonlinear Bayesian filtering algorithms are overwhelmed by data volume, velocity and increasing complexity of computational models. In this paper, we propose a novel ensemble based nonlinear Bayesian filtering approach which only requires a small number of simulations and can be applied to high-dimensional systems in the presence of intractable likelihood functions. The proposed approach uses linear latent projections to estimate the joint probability distribution between states, parameters, and observables using a mixture of Gaussian components generated by the reconstruction error "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.02340","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":"2026-05-17T23:44:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"71fTaQQzkeqPjhw9jdP+YHHbdRHp90N7hByXwb+lUa9GLDrk9fqbGfBJBsoN9RJtSd5T8caQaLBOM04XYudQBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-12T09:54:34.600254Z"},"content_sha256":"cdd0215f3c354bf6a78998a8fb2d9c49d358e9316722a5d52a082e190b52f1ed","schema_version":"1.0","event_id":"sha256:cdd0215f3c354bf6a78998a8fb2d9c49d358e9316722a5d52a082e190b52f1ed"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3XHYTNWOLUQ6PRS3QYQBSUVDFL/bundle.json","state_url":"https://pith.science/pith/3XHYTNWOLUQ6PRS3QYQBSUVDFL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3XHYTNWOLUQ6PRS3QYQBSUVDFL/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":"2026-06-12T09:54:34Z","links":{"resolver":"https://pith.science/pith/3XHYTNWOLUQ6PRS3QYQBSUVDFL","bundle":"https://pith.science/pith/3XHYTNWOLUQ6PRS3QYQBSUVDFL/bundle.json","state":"https://pith.science/pith/3XHYTNWOLUQ6PRS3QYQBSUVDFL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3XHYTNWOLUQ6PRS3QYQBSUVDFL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:3XHYTNWOLUQ6PRS3QYQBSUVDFL","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":"e6aaad8b434b4a665f46087abf9ab3bbbcbfbaa4c3637680417a603480094257","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-08-08T00:45:18Z","title_canon_sha256":"b0fd41e446ae1710b0c80b46b7f8e2b8074b5cad6495fbcfc92a0d21d414dce5"},"schema_version":"1.0","source":{"id":"1708.02340","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.02340","created_at":"2026-05-17T23:44:22Z"},{"alias_kind":"arxiv_version","alias_value":"1708.02340v2","created_at":"2026-05-17T23:44:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.02340","created_at":"2026-05-17T23:44:22Z"},{"alias_kind":"pith_short_12","alias_value":"3XHYTNWOLUQ6","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"3XHYTNWOLUQ6PRS3","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"3XHYTNWO","created_at":"2026-05-18T12:30:58Z"}],"graph_snapshots":[{"event_id":"sha256:cdd0215f3c354bf6a78998a8fb2d9c49d358e9316722a5d52a082e190b52f1ed","target":"graph","created_at":"2026-05-17T23:44:22Z","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":"Real-time nonlinear Bayesian filtering algorithms are overwhelmed by data volume, velocity and increasing complexity of computational models. In this paper, we propose a novel ensemble based nonlinear Bayesian filtering approach which only requires a small number of simulations and can be applied to high-dimensional systems in the presence of intractable likelihood functions. The proposed approach uses linear latent projections to estimate the joint probability distribution between states, parameters, and observables using a mixture of Gaussian components generated by the reconstruction error ","authors_text":"Gabriel Terejanu, Xiao Lin","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-08-08T00:45:18Z","title":"EnLLVM: Ensemble Based Nonlinear Bayesian Filtering Using Linear Latent Variable Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.02340","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:acd3660840c50d3db90df02a2248f0e43852d46d386bd63447ed0cdf155b961b","target":"record","created_at":"2026-05-17T23:44:22Z","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":"e6aaad8b434b4a665f46087abf9ab3bbbcbfbaa4c3637680417a603480094257","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-08-08T00:45:18Z","title_canon_sha256":"b0fd41e446ae1710b0c80b46b7f8e2b8074b5cad6495fbcfc92a0d21d414dce5"},"schema_version":"1.0","source":{"id":"1708.02340","kind":"arxiv","version":2}},"canonical_sha256":"ddcf89b6ce5d21e7c65b86201952a32afa2a1fbfa7b5b8ed54933fe99d3ef871","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ddcf89b6ce5d21e7c65b86201952a32afa2a1fbfa7b5b8ed54933fe99d3ef871","first_computed_at":"2026-05-17T23:44:22.997878Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:22.997878Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dxrybrvgS2B1qTYfUkobTsWdH6McwgZrFnwUxoEgHbe92BGWjolVlM4kquCleTu82itHUBrlT34/uvLMSCt8Bw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:22.998530Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.02340","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:acd3660840c50d3db90df02a2248f0e43852d46d386bd63447ed0cdf155b961b","sha256:cdd0215f3c354bf6a78998a8fb2d9c49d358e9316722a5d52a082e190b52f1ed"],"state_sha256":"ffae63d0947576fb80f689d67a1a92e6ce11e684fb4c8dd1d095165c6e9d90a8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WMuLnc+8C3LsAXFfWfCzqAcLMPvGH1Jx4nuAp/5SgqQG6j4jWUrQ0FFp10DOWRqKB5OFTxIHgLBDS3efHsMbBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-12T09:54:34.602299Z","bundle_sha256":"18e7a75d846c171cc7cee123000b8aa6645d45ea06d541f5f223cdb7474fb512"}}