{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:KSTVT7ZNURLD6F6CV7BSUASA2O","short_pith_number":"pith:KSTVT7ZN","canonical_record":{"source":{"id":"1510.05239","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-10-18T13:00:38Z","cross_cats_sorted":[],"title_canon_sha256":"4c1fd986c15029a2238482eb8d008f3c6679b2bfdea71e6764f9c724dd12f0b8","abstract_canon_sha256":"2d6082d84832fb990161706d432c65cf123f0fbf73194a08f6fc6249f14bcb06"},"schema_version":"1.0"},"canonical_sha256":"54a759ff2da4563f17c2afc32a0240d3951b1e6ac37ad8c9267b1d98461fc1b2","source":{"kind":"arxiv","id":"1510.05239","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1510.05239","created_at":"2026-05-18T01:12:07Z"},{"alias_kind":"arxiv_version","alias_value":"1510.05239v2","created_at":"2026-05-18T01:12:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.05239","created_at":"2026-05-18T01:12:07Z"},{"alias_kind":"pith_short_12","alias_value":"KSTVT7ZNURLD","created_at":"2026-05-18T12:29:29Z"},{"alias_kind":"pith_short_16","alias_value":"KSTVT7ZNURLD6F6C","created_at":"2026-05-18T12:29:29Z"},{"alias_kind":"pith_short_8","alias_value":"KSTVT7ZN","created_at":"2026-05-18T12:29:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:KSTVT7ZNURLD6F6CV7BSUASA2O","target":"record","payload":{"canonical_record":{"source":{"id":"1510.05239","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-10-18T13:00:38Z","cross_cats_sorted":[],"title_canon_sha256":"4c1fd986c15029a2238482eb8d008f3c6679b2bfdea71e6764f9c724dd12f0b8","abstract_canon_sha256":"2d6082d84832fb990161706d432c65cf123f0fbf73194a08f6fc6249f14bcb06"},"schema_version":"1.0"},"canonical_sha256":"54a759ff2da4563f17c2afc32a0240d3951b1e6ac37ad8c9267b1d98461fc1b2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:12:07.752197Z","signature_b64":"kZ9gnMci2IPxMfXuneh9CgZfWhLA/hkV0NuB9RlZUODarnZd242cjIdU6W2RNqftV+czmNYyQ98gfio9tda7Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"54a759ff2da4563f17c2afc32a0240d3951b1e6ac37ad8c9267b1d98461fc1b2","last_reissued_at":"2026-05-18T01:12:07.751725Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:12:07.751725Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1510.05239","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":"2026-05-18T01:12:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oZMNrX3k9ZkseJZdLCKM4A2tKxhDYwAfEim9Uil1nR6jjbo7jKbrV0UBAf7xhDDS5ATbbMo8h5bHzReUioSEAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T16:55:55.568053Z"},"content_sha256":"7acf3bd3943af5959f1794df6bc9b65dc49325feb43c58baed96f95ab40384a0","schema_version":"1.0","event_id":"sha256:7acf3bd3943af5959f1794df6bc9b65dc49325feb43c58baed96f95ab40384a0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:KSTVT7ZNURLD6F6CV7BSUASA2O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A TV-Gaussian prior for infinite-dimensional Bayesian inverse problems and its numerical implementations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Jinglai Li, Zhewei Yao, Zixi Hu","submitted_at":"2015-10-18T13:00:38Z","abstract_excerpt":"Many scientific and engineering problems require to perform Bayesian inferences in function spaces, in which the unknowns are of infinite dimension. In such problems, choosing an appropriate prior distribution is an important task. In particular we consider problems where the function to infer is subject to sharp jumps which render the commonly used Gaussian measures unsuitable. On the other hand, the so-called total variation (TV) prior can only be defined in a finite dimensional setting, and does not lead to a well-defined posterior measure in function spaces. In this work we present a TV-Ga"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.05239","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":"2026-05-18T01:12:07Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"InN090663LMIV8sNacbg09N1f5Ihvcj2+xiODN77YeKtSQrQaVDe749L0EiG/dQA94l10vZKfUfDWfUousc1AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-19T16:55:55.568416Z"},"content_sha256":"11d79da82c10b247069b6c9fb50365eaf5da664b804c23a469a39190686390e0","schema_version":"1.0","event_id":"sha256:11d79da82c10b247069b6c9fb50365eaf5da664b804c23a469a39190686390e0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KSTVT7ZNURLD6F6CV7BSUASA2O/bundle.json","state_url":"https://pith.science/pith/KSTVT7ZNURLD6F6CV7BSUASA2O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KSTVT7ZNURLD6F6CV7BSUASA2O/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":"2026-05-19T16:55:55Z","links":{"resolver":"https://pith.science/pith/KSTVT7ZNURLD6F6CV7BSUASA2O","bundle":"https://pith.science/pith/KSTVT7ZNURLD6F6CV7BSUASA2O/bundle.json","state":"https://pith.science/pith/KSTVT7ZNURLD6F6CV7BSUASA2O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KSTVT7ZNURLD6F6CV7BSUASA2O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:KSTVT7ZNURLD6F6CV7BSUASA2O","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":"2d6082d84832fb990161706d432c65cf123f0fbf73194a08f6fc6249f14bcb06","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-10-18T13:00:38Z","title_canon_sha256":"4c1fd986c15029a2238482eb8d008f3c6679b2bfdea71e6764f9c724dd12f0b8"},"schema_version":"1.0","source":{"id":"1510.05239","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1510.05239","created_at":"2026-05-18T01:12:07Z"},{"alias_kind":"arxiv_version","alias_value":"1510.05239v2","created_at":"2026-05-18T01:12:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.05239","created_at":"2026-05-18T01:12:07Z"},{"alias_kind":"pith_short_12","alias_value":"KSTVT7ZNURLD","created_at":"2026-05-18T12:29:29Z"},{"alias_kind":"pith_short_16","alias_value":"KSTVT7ZNURLD6F6C","created_at":"2026-05-18T12:29:29Z"},{"alias_kind":"pith_short_8","alias_value":"KSTVT7ZN","created_at":"2026-05-18T12:29:29Z"}],"graph_snapshots":[{"event_id":"sha256:11d79da82c10b247069b6c9fb50365eaf5da664b804c23a469a39190686390e0","target":"graph","created_at":"2026-05-18T01:12:07Z","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":"Many scientific and engineering problems require to perform Bayesian inferences in function spaces, in which the unknowns are of infinite dimension. In such problems, choosing an appropriate prior distribution is an important task. In particular we consider problems where the function to infer is subject to sharp jumps which render the commonly used Gaussian measures unsuitable. On the other hand, the so-called total variation (TV) prior can only be defined in a finite dimensional setting, and does not lead to a well-defined posterior measure in function spaces. In this work we present a TV-Ga","authors_text":"Jinglai Li, Zhewei Yao, Zixi Hu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-10-18T13:00:38Z","title":"A TV-Gaussian prior for infinite-dimensional Bayesian inverse problems and its numerical implementations"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.05239","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":"sha256:7acf3bd3943af5959f1794df6bc9b65dc49325feb43c58baed96f95ab40384a0","target":"record","created_at":"2026-05-18T01:12:07Z","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":"2d6082d84832fb990161706d432c65cf123f0fbf73194a08f6fc6249f14bcb06","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-10-18T13:00:38Z","title_canon_sha256":"4c1fd986c15029a2238482eb8d008f3c6679b2bfdea71e6764f9c724dd12f0b8"},"schema_version":"1.0","source":{"id":"1510.05239","kind":"arxiv","version":2}},"canonical_sha256":"54a759ff2da4563f17c2afc32a0240d3951b1e6ac37ad8c9267b1d98461fc1b2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"54a759ff2da4563f17c2afc32a0240d3951b1e6ac37ad8c9267b1d98461fc1b2","first_computed_at":"2026-05-18T01:12:07.751725Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:12:07.751725Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"kZ9gnMci2IPxMfXuneh9CgZfWhLA/hkV0NuB9RlZUODarnZd242cjIdU6W2RNqftV+czmNYyQ98gfio9tda7Bw==","signature_status":"signed_v1","signed_at":"2026-05-18T01:12:07.752197Z","signed_message":"canonical_sha256_bytes"},"source_id":"1510.05239","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7acf3bd3943af5959f1794df6bc9b65dc49325feb43c58baed96f95ab40384a0","sha256:11d79da82c10b247069b6c9fb50365eaf5da664b804c23a469a39190686390e0"],"state_sha256":"4722de13523388feb2df43bc3c8d9f3beb3f0f7519b8478e69c4c5771804cb51"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LeTMgr13EqOU+ev3fIekN03F0SRd0HEfyLO7QEWZtanEFbVX839J6YL/voD2tsBCBDeNTZiodjAWBw1of41SBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-19T16:55:55.570452Z","bundle_sha256":"fd7b6fec3dddc363460543882f8f39c8004bfc1f7b64946f0f80d7ce5aadff21"}}