{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:EV4SMJKSIMUJ3DEH24XZGEZJL3","short_pith_number":"pith:EV4SMJKS","canonical_record":{"source":{"id":"1802.08167","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-22T16:53:09Z","cross_cats_sorted":[],"title_canon_sha256":"f4b3cd2769fe3da079a5023f3fce5505bbd153b749cdc5fd79d546d187ee00e8","abstract_canon_sha256":"ad3c5341714af061e05917ba520fd40026a20c318c80d15f97687d042cf60171"},"schema_version":"1.0"},"canonical_sha256":"257926255243289d8c87d72f9313295ee017a7c55eb759b45817c18271e475ea","source":{"kind":"arxiv","id":"1802.08167","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.08167","created_at":"2026-05-18T00:22:45Z"},{"alias_kind":"arxiv_version","alias_value":"1802.08167v1","created_at":"2026-05-18T00:22:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.08167","created_at":"2026-05-18T00:22:45Z"},{"alias_kind":"pith_short_12","alias_value":"EV4SMJKSIMUJ","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EV4SMJKSIMUJ3DEH","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EV4SMJKS","created_at":"2026-05-18T12:32:22Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:EV4SMJKSIMUJ3DEH24XZGEZJL3","target":"record","payload":{"canonical_record":{"source":{"id":"1802.08167","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-22T16:53:09Z","cross_cats_sorted":[],"title_canon_sha256":"f4b3cd2769fe3da079a5023f3fce5505bbd153b749cdc5fd79d546d187ee00e8","abstract_canon_sha256":"ad3c5341714af061e05917ba520fd40026a20c318c80d15f97687d042cf60171"},"schema_version":"1.0"},"canonical_sha256":"257926255243289d8c87d72f9313295ee017a7c55eb759b45817c18271e475ea","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:45.358160Z","signature_b64":"CVFfhSs2jIcPymlgVo1RgKrIOcUskDsTZcW8PU0QhmAfHJbh9MmubivCWTf0H18Mz/4W2L5uBxVIiyU+mdREAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"257926255243289d8c87d72f9313295ee017a7c55eb759b45817c18271e475ea","last_reissued_at":"2026-05-18T00:22:45.357707Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:45.357707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.08167","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:22:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wy0Lvib2P75c2hFZXMcFhz2JeZy4hLqS8rcKAIdkRNtW+zr6EF/kADrAs6c3pbbvZHo8FtgXfLhgAn4/o6mTAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T07:16:54.773630Z"},"content_sha256":"8ad5a901388584d26cdb5800ad7ff8e5cf5506b7ab88883daaf45afa08f025a4","schema_version":"1.0","event_id":"sha256:8ad5a901388584d26cdb5800ad7ff8e5cf5506b7ab88883daaf45afa08f025a4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:EV4SMJKSIMUJ3DEH24XZGEZJL3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Causally-Generated Stationary Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Richard E. Turner, Wessel Bruinsma","submitted_at":"2018-02-22T16:53:09Z","abstract_excerpt":"We present the Causal Gaussian Process Convolution Model (CGPCM), a doubly nonparametric model for causal, spectrally complex dynamical phenomena. The CGPCM is a generative model in which white noise is passed through a causal, nonparametric-window moving-average filter, a construction that we show to be equivalent to a Gaussian process with a nonparametric kernel that is biased towards causally-generated signals. We develop enhanced variational inference and learning schemes for the CGPCM and its previous acausal variant, the GPCM (Tobar et al., 2015b), that significantly improve statistical "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.08167","kind":"arxiv","version":1},"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":"2026-05-18T00:22:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oRlIYVD158/jNy8c2Ba8SSnxVuQP2K7neEe3I+T7HAKk+CoMeQvRL1+5fOaPs5JCVwzv1Kxv7GAM9N3TSYq7CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T07:16:54.774281Z"},"content_sha256":"aab6d5a1469bf2697f9a4c550dce567eeec55d296ce3a087cb0976384dae4dfd","schema_version":"1.0","event_id":"sha256:aab6d5a1469bf2697f9a4c550dce567eeec55d296ce3a087cb0976384dae4dfd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EV4SMJKSIMUJ3DEH24XZGEZJL3/bundle.json","state_url":"https://pith.science/pith/EV4SMJKSIMUJ3DEH24XZGEZJL3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EV4SMJKSIMUJ3DEH24XZGEZJL3/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":"2026-06-07T07:16:54Z","links":{"resolver":"https://pith.science/pith/EV4SMJKSIMUJ3DEH24XZGEZJL3","bundle":"https://pith.science/pith/EV4SMJKSIMUJ3DEH24XZGEZJL3/bundle.json","state":"https://pith.science/pith/EV4SMJKSIMUJ3DEH24XZGEZJL3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EV4SMJKSIMUJ3DEH24XZGEZJL3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:EV4SMJKSIMUJ3DEH24XZGEZJL3","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":"ad3c5341714af061e05917ba520fd40026a20c318c80d15f97687d042cf60171","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-22T16:53:09Z","title_canon_sha256":"f4b3cd2769fe3da079a5023f3fce5505bbd153b749cdc5fd79d546d187ee00e8"},"schema_version":"1.0","source":{"id":"1802.08167","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.08167","created_at":"2026-05-18T00:22:45Z"},{"alias_kind":"arxiv_version","alias_value":"1802.08167v1","created_at":"2026-05-18T00:22:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.08167","created_at":"2026-05-18T00:22:45Z"},{"alias_kind":"pith_short_12","alias_value":"EV4SMJKSIMUJ","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_16","alias_value":"EV4SMJKSIMUJ3DEH","created_at":"2026-05-18T12:32:22Z"},{"alias_kind":"pith_short_8","alias_value":"EV4SMJKS","created_at":"2026-05-18T12:32:22Z"}],"graph_snapshots":[{"event_id":"sha256:aab6d5a1469bf2697f9a4c550dce567eeec55d296ce3a087cb0976384dae4dfd","target":"graph","created_at":"2026-05-18T00:22:45Z","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":"We present the Causal Gaussian Process Convolution Model (CGPCM), a doubly nonparametric model for causal, spectrally complex dynamical phenomena. The CGPCM is a generative model in which white noise is passed through a causal, nonparametric-window moving-average filter, a construction that we show to be equivalent to a Gaussian process with a nonparametric kernel that is biased towards causally-generated signals. We develop enhanced variational inference and learning schemes for the CGPCM and its previous acausal variant, the GPCM (Tobar et al., 2015b), that significantly improve statistical ","authors_text":"Richard E. Turner, Wessel Bruinsma","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-22T16:53:09Z","title":"Learning Causally-Generated Stationary Time Series"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.08167","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":"sha256:8ad5a901388584d26cdb5800ad7ff8e5cf5506b7ab88883daaf45afa08f025a4","target":"record","created_at":"2026-05-18T00:22:45Z","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":"ad3c5341714af061e05917ba520fd40026a20c318c80d15f97687d042cf60171","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-22T16:53:09Z","title_canon_sha256":"f4b3cd2769fe3da079a5023f3fce5505bbd153b749cdc5fd79d546d187ee00e8"},"schema_version":"1.0","source":{"id":"1802.08167","kind":"arxiv","version":1}},"canonical_sha256":"257926255243289d8c87d72f9313295ee017a7c55eb759b45817c18271e475ea","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"257926255243289d8c87d72f9313295ee017a7c55eb759b45817c18271e475ea","first_computed_at":"2026-05-18T00:22:45.357707Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:22:45.357707Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"CVFfhSs2jIcPymlgVo1RgKrIOcUskDsTZcW8PU0QhmAfHJbh9MmubivCWTf0H18Mz/4W2L5uBxVIiyU+mdREAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:22:45.358160Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.08167","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8ad5a901388584d26cdb5800ad7ff8e5cf5506b7ab88883daaf45afa08f025a4","sha256:aab6d5a1469bf2697f9a4c550dce567eeec55d296ce3a087cb0976384dae4dfd"],"state_sha256":"1948e4dd287bf593f2d0dd8dd48faf8970548f9282fd5359311d60e44042e31e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TJkG9MEnO6a2nH47iplzquUj0JKRPBRUkrhDMpGn5WOpB8xmKlu9IqEfRSAEkOqDf2V7SZdhU26olyl8qvUSDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T07:16:54.777749Z","bundle_sha256":"3d0c1fa53cb3d58f78a129a5a4082cb49b672ca7cfbbac09e3da8675ebc8c8d4"}}