{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:NMLYQFYDHYWNENXXYJAJTOYNJQ","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":"5915631b5623b0b4067d94c42efa55451f80d282a2b9235128e5921c9edb4f5b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-06-07T13:57:51Z","title_canon_sha256":"0c25e8b72b9b8da97c5aa87a192d83525c3dda7985f5f3a7e5be15fe8ff21c16"},"schema_version":"1.0","source":{"id":"1506.02267","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1506.02267","created_at":"2026-05-18T01:17:05Z"},{"alias_kind":"arxiv_version","alias_value":"1506.02267v2","created_at":"2026-05-18T01:17:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.02267","created_at":"2026-05-18T01:17:05Z"},{"alias_kind":"pith_short_12","alias_value":"NMLYQFYDHYWN","created_at":"2026-05-18T12:29:32Z"},{"alias_kind":"pith_short_16","alias_value":"NMLYQFYDHYWNENXX","created_at":"2026-05-18T12:29:32Z"},{"alias_kind":"pith_short_8","alias_value":"NMLYQFYD","created_at":"2026-05-18T12:29:32Z"}],"graph_snapshots":[{"event_id":"sha256:30d34ac62b910d6642e0a1aba6c7329fc6e0a6f50641f1be0118c5dce1b21966","target":"graph","created_at":"2026-05-18T01:17:05Z","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":"Gaussian processes allow for flexible specification of prior assumptions of unknown dynamics in state space models. We present a procedure for efficient Bayesian learning in Gaussian process state space models, where the representation is formed by projecting the problem onto a set of approximate eigenfunctions derived from the prior covariance structure. Learning under this family of models can be conducted using a carefully crafted particle MCMC algorithm. This scheme is computationally efficient and yet allows for a fully Bayesian treatment of the problem. Compared to conventional system id","authors_text":"Andreas Svensson, Arno Solin, Simo S\\\"arkk\\\"a, Thomas B. Sch\\\"on","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-06-07T13:57:51Z","title":"Computationally Efficient Bayesian Learning of Gaussian Process State Space Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.02267","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:b80ce57ccd3f056a2475313f09d9d5ed4b2bc7a6eae9caf415ba705d555f300a","target":"record","created_at":"2026-05-18T01:17:05Z","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":"5915631b5623b0b4067d94c42efa55451f80d282a2b9235128e5921c9edb4f5b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-06-07T13:57:51Z","title_canon_sha256":"0c25e8b72b9b8da97c5aa87a192d83525c3dda7985f5f3a7e5be15fe8ff21c16"},"schema_version":"1.0","source":{"id":"1506.02267","kind":"arxiv","version":2}},"canonical_sha256":"6b178817033e2cd236f7c24099bb0d4c3949e1d2a4d99d24bdb13add154e2084","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6b178817033e2cd236f7c24099bb0d4c3949e1d2a4d99d24bdb13add154e2084","first_computed_at":"2026-05-18T01:17:05.338905Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:17:05.338905Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zvZ28PFxPSz+cW7jYEn6eBVYe5NLkS9n2n37/JPpGKjxwB4vImgwZgEh7WhztG0Xs7+EMjhLrchKYgeLYPF2DQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:17:05.339661Z","signed_message":"canonical_sha256_bytes"},"source_id":"1506.02267","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b80ce57ccd3f056a2475313f09d9d5ed4b2bc7a6eae9caf415ba705d555f300a","sha256:30d34ac62b910d6642e0a1aba6c7329fc6e0a6f50641f1be0118c5dce1b21966"],"state_sha256":"b23eec20f3308ac0533f66cc2a8dc9755a825be8d1b7cae447ce0f767428b763"}