{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:UIGP6POJUEFOJYN4IDN3E7ZL73","short_pith_number":"pith:UIGP6POJ","canonical_record":{"source":{"id":"1206.0262","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2012-06-01T17:49:39Z","cross_cats_sorted":["math.PR","math.ST","stat.TH"],"title_canon_sha256":"5975c30066863a7263cf30dee9e28ee66317846ccc55ce3acbbca33d238c84ba","abstract_canon_sha256":"c8a659cafa9f0d2c9c49c0465a2a305204d6acd65483581eb933a7c733bb814e"},"schema_version":"1.0"},"canonical_sha256":"a20cff3dc9a10ae4e1bc40dbb27f2bfed4542ee2450bb76961a2e71eb3b79b0b","source":{"kind":"arxiv","id":"1206.0262","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1206.0262","created_at":"2026-05-18T02:35:52Z"},{"alias_kind":"arxiv_version","alias_value":"1206.0262v2","created_at":"2026-05-18T02:35:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1206.0262","created_at":"2026-05-18T02:35:52Z"},{"alias_kind":"pith_short_12","alias_value":"UIGP6POJUEFO","created_at":"2026-05-18T12:27:23Z"},{"alias_kind":"pith_short_16","alias_value":"UIGP6POJUEFOJYN4","created_at":"2026-05-18T12:27:23Z"},{"alias_kind":"pith_short_8","alias_value":"UIGP6POJ","created_at":"2026-05-18T12:27:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:UIGP6POJUEFOJYN4IDN3E7ZL73","target":"record","payload":{"canonical_record":{"source":{"id":"1206.0262","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2012-06-01T17:49:39Z","cross_cats_sorted":["math.PR","math.ST","stat.TH"],"title_canon_sha256":"5975c30066863a7263cf30dee9e28ee66317846ccc55ce3acbbca33d238c84ba","abstract_canon_sha256":"c8a659cafa9f0d2c9c49c0465a2a305204d6acd65483581eb933a7c733bb814e"},"schema_version":"1.0"},"canonical_sha256":"a20cff3dc9a10ae4e1bc40dbb27f2bfed4542ee2450bb76961a2e71eb3b79b0b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:35:52.748541Z","signature_b64":"LUT+we2hQ7vjUKwoqh9ycZh2tLCER2X863VboBVs57MVLi7LaDOMIFEvELJBHUAENqVvjn4xhr4Mb68DVDU6CA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a20cff3dc9a10ae4e1bc40dbb27f2bfed4542ee2450bb76961a2e71eb3b79b0b","last_reissued_at":"2026-05-18T02:35:52.748119Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:35:52.748119Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1206.0262","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":"2026-05-18T02:35:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TjT9+9CwqTpN4alEo5EbV088yFasuieCSgD8laP0B1easrc//WylBba2UcgBGbElL26Y3tFNgiw3yOn/XhFjAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T22:37:09.142199Z"},"content_sha256":"1cc4dc6fb0deaa846921a9d63d85d2a75891f713eaa16d16af73e5f9b97de93b","schema_version":"1.0","event_id":"sha256:1cc4dc6fb0deaa846921a9d63d85d2a75891f713eaa16d16af73e5f9b97de93b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:UIGP6POJUEFOJYN4IDN3E7ZL73","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast Markov chain Monte Carlo sampling for sparse Bayesian inference in high-dimensional inverse problems using L1-type priors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.PR","math.ST","stat.TH"],"primary_cat":"math.NA","authors_text":"Applied Mathematics, Biosignalanalysis, Felix Lucka (Institute for Computational, Germany), Institute for Biomagnetism, University of M\\\"unster","submitted_at":"2012-06-01T17:49:39Z","abstract_excerpt":"Sparsity has become a key concept for solving of high-dimensional inverse problems using variational regularization techniques. Recently, using similar sparsity-constraints in the Bayesian framework for inverse problems by encoding them in the prior distribution has attracted attention. Important questions about the relation between regularization theory and Bayesian inference still need to be addressed when using sparsity promoting inversion. A practical obstacle for these examinations is the lack of fast posterior sampling algorithms for sparse, high-dimensional Bayesian inversion: Accessing"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1206.0262","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":"2026-05-18T02:35:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qJdNix1+3G4hivX9McqH1ryDDxSuSNMAOJZRwAYXWmW9isM0mB0u32l1cMUmkKXy89LwMF0AeuvLbBTw7JDbCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-01T22:37:09.142564Z"},"content_sha256":"e69c1bd3e8cdbd3d51cbba3d34454673c52e8c4c191ff7980154d75c042c90ad","schema_version":"1.0","event_id":"sha256:e69c1bd3e8cdbd3d51cbba3d34454673c52e8c4c191ff7980154d75c042c90ad"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UIGP6POJUEFOJYN4IDN3E7ZL73/bundle.json","state_url":"https://pith.science/pith/UIGP6POJUEFOJYN4IDN3E7ZL73/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UIGP6POJUEFOJYN4IDN3E7ZL73/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":"2026-06-01T22:37:09Z","links":{"resolver":"https://pith.science/pith/UIGP6POJUEFOJYN4IDN3E7ZL73","bundle":"https://pith.science/pith/UIGP6POJUEFOJYN4IDN3E7ZL73/bundle.json","state":"https://pith.science/pith/UIGP6POJUEFOJYN4IDN3E7ZL73/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UIGP6POJUEFOJYN4IDN3E7ZL73/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:UIGP6POJUEFOJYN4IDN3E7ZL73","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":"c8a659cafa9f0d2c9c49c0465a2a305204d6acd65483581eb933a7c733bb814e","cross_cats_sorted":["math.PR","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2012-06-01T17:49:39Z","title_canon_sha256":"5975c30066863a7263cf30dee9e28ee66317846ccc55ce3acbbca33d238c84ba"},"schema_version":"1.0","source":{"id":"1206.0262","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1206.0262","created_at":"2026-05-18T02:35:52Z"},{"alias_kind":"arxiv_version","alias_value":"1206.0262v2","created_at":"2026-05-18T02:35:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1206.0262","created_at":"2026-05-18T02:35:52Z"},{"alias_kind":"pith_short_12","alias_value":"UIGP6POJUEFO","created_at":"2026-05-18T12:27:23Z"},{"alias_kind":"pith_short_16","alias_value":"UIGP6POJUEFOJYN4","created_at":"2026-05-18T12:27:23Z"},{"alias_kind":"pith_short_8","alias_value":"UIGP6POJ","created_at":"2026-05-18T12:27:23Z"}],"graph_snapshots":[{"event_id":"sha256:e69c1bd3e8cdbd3d51cbba3d34454673c52e8c4c191ff7980154d75c042c90ad","target":"graph","created_at":"2026-05-18T02:35:52Z","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":"Sparsity has become a key concept for solving of high-dimensional inverse problems using variational regularization techniques. Recently, using similar sparsity-constraints in the Bayesian framework for inverse problems by encoding them in the prior distribution has attracted attention. Important questions about the relation between regularization theory and Bayesian inference still need to be addressed when using sparsity promoting inversion. A practical obstacle for these examinations is the lack of fast posterior sampling algorithms for sparse, high-dimensional Bayesian inversion: Accessing","authors_text":"Applied Mathematics, Biosignalanalysis, Felix Lucka (Institute for Computational, Germany), Institute for Biomagnetism, University of M\\\"unster","cross_cats":["math.PR","math.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2012-06-01T17:49:39Z","title":"Fast Markov chain Monte Carlo sampling for sparse Bayesian inference in high-dimensional inverse problems using L1-type priors"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1206.0262","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":"sha256:1cc4dc6fb0deaa846921a9d63d85d2a75891f713eaa16d16af73e5f9b97de93b","target":"record","created_at":"2026-05-18T02:35:52Z","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":"c8a659cafa9f0d2c9c49c0465a2a305204d6acd65483581eb933a7c733bb814e","cross_cats_sorted":["math.PR","math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2012-06-01T17:49:39Z","title_canon_sha256":"5975c30066863a7263cf30dee9e28ee66317846ccc55ce3acbbca33d238c84ba"},"schema_version":"1.0","source":{"id":"1206.0262","kind":"arxiv","version":2}},"canonical_sha256":"a20cff3dc9a10ae4e1bc40dbb27f2bfed4542ee2450bb76961a2e71eb3b79b0b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a20cff3dc9a10ae4e1bc40dbb27f2bfed4542ee2450bb76961a2e71eb3b79b0b","first_computed_at":"2026-05-18T02:35:52.748119Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:35:52.748119Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LUT+we2hQ7vjUKwoqh9ycZh2tLCER2X863VboBVs57MVLi7LaDOMIFEvELJBHUAENqVvjn4xhr4Mb68DVDU6CA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:35:52.748541Z","signed_message":"canonical_sha256_bytes"},"source_id":"1206.0262","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1cc4dc6fb0deaa846921a9d63d85d2a75891f713eaa16d16af73e5f9b97de93b","sha256:e69c1bd3e8cdbd3d51cbba3d34454673c52e8c4c191ff7980154d75c042c90ad"],"state_sha256":"03499a4ce78e349f3cd1d3f4c60ca0ecb988858b4069a7f64171882c940640b7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"29wY7pEOHt5TzYEcBrB9Bxz43UdjdTpjlM+kjrPtNiPfiM8ferEZ8bUq8vm91RcpHtqFkBuUtVLJHyIjiaDdBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-01T22:37:09.144546Z","bundle_sha256":"8b40a2c02c6cd8e3159e65ee98b5eaa63cc48bb4d8e1cd2d1cfba7d72a6c45ac"}}