{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LM33NZZMUJX7ROK3XAF7QSRCUB","short_pith_number":"pith:LM33NZZM","schema_version":"1.0","canonical_sha256":"5b37b6e72ca26ff8b95bb80bf84a22a041795a84d552e22a8868d881b7457866","source":{"kind":"arxiv","id":"2601.20888","version":3},"attestation_state":"computed","paper":{"title":"Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.CO","stat.TH"],"primary_cat":"stat.ML","authors_text":"George Biros, Youguang Chen","submitted_at":"2026-01-28T03:44:01Z","abstract_excerpt":"We study sampling from posterior distributions in Bayesian linear inverse problems where $A$, the parameters to observables operator, is computationally expensive. In many applications, $A$ can be factored in a manner that facilitates the construction of a cost-effective approximation $\\tilde{A}$. In this framework, we introduce Latent-IMH, a sampling method based on the Metropolis-Hastings independence (IMH) sampler. Latent-IMH first generates intermediate latent variables using the approximate $\\tilde{A}$, and then refines them using the exact $A$. Its primary benefit is that it shifts the c"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2601.20888","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-01-28T03:44:01Z","cross_cats_sorted":["cs.LG","math.ST","stat.CO","stat.TH"],"title_canon_sha256":"7b1706221965527757ec5e08a15ea75769b51c9f381a6d8ec6b911459f25c527","abstract_canon_sha256":"129e13008221ff3875f26ad3e718c898168a37fb46febff3798975e0a0a1ac7d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T00:04:23.586899Z","signature_b64":"VPbiUslgWehdydfrMx7N0Oom1b+oXaoznd0rBRM5sUCEnYlpxcFhnqlnaCjMTKAYbejvlBIOjCPjpcZTURXVCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5b37b6e72ca26ff8b95bb80bf84a22a041795a84d552e22a8868d881b7457866","last_reissued_at":"2026-05-20T00:04:23.586010Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T00:04:23.586010Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Latent-IMH: Efficient Bayesian Inference for Inverse Problems with Approximate Operators","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","math.ST","stat.CO","stat.TH"],"primary_cat":"stat.ML","authors_text":"George Biros, Youguang Chen","submitted_at":"2026-01-28T03:44:01Z","abstract_excerpt":"We study sampling from posterior distributions in Bayesian linear inverse problems where $A$, the parameters to observables operator, is computationally expensive. In many applications, $A$ can be factored in a manner that facilitates the construction of a cost-effective approximation $\\tilde{A}$. In this framework, we introduce Latent-IMH, a sampling method based on the Metropolis-Hastings independence (IMH) sampler. Latent-IMH first generates intermediate latent variables using the approximate $\\tilde{A}$, and then refines them using the exact $A$. Its primary benefit is that it shifts the c"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2601.20888","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2601.20888/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2601.20888","created_at":"2026-05-20T00:04:23.586156+00:00"},{"alias_kind":"arxiv_version","alias_value":"2601.20888v3","created_at":"2026-05-20T00:04:23.586156+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2601.20888","created_at":"2026-05-20T00:04:23.586156+00:00"},{"alias_kind":"pith_short_12","alias_value":"LM33NZZMUJX7","created_at":"2026-05-20T00:04:23.586156+00:00"},{"alias_kind":"pith_short_16","alias_value":"LM33NZZMUJX7ROK3","created_at":"2026-05-20T00:04:23.586156+00:00"},{"alias_kind":"pith_short_8","alias_value":"LM33NZZM","created_at":"2026-05-20T00:04:23.586156+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LM33NZZMUJX7ROK3XAF7QSRCUB","json":"https://pith.science/pith/LM33NZZMUJX7ROK3XAF7QSRCUB.json","graph_json":"https://pith.science/api/pith-number/LM33NZZMUJX7ROK3XAF7QSRCUB/graph.json","events_json":"https://pith.science/api/pith-number/LM33NZZMUJX7ROK3XAF7QSRCUB/events.json","paper":"https://pith.science/paper/LM33NZZM"},"agent_actions":{"view_html":"https://pith.science/pith/LM33NZZMUJX7ROK3XAF7QSRCUB","download_json":"https://pith.science/pith/LM33NZZMUJX7ROK3XAF7QSRCUB.json","view_paper":"https://pith.science/paper/LM33NZZM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2601.20888&json=true","fetch_graph":"https://pith.science/api/pith-number/LM33NZZMUJX7ROK3XAF7QSRCUB/graph.json","fetch_events":"https://pith.science/api/pith-number/LM33NZZMUJX7ROK3XAF7QSRCUB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LM33NZZMUJX7ROK3XAF7QSRCUB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LM33NZZMUJX7ROK3XAF7QSRCUB/action/storage_attestation","attest_author":"https://pith.science/pith/LM33NZZMUJX7ROK3XAF7QSRCUB/action/author_attestation","sign_citation":"https://pith.science/pith/LM33NZZMUJX7ROK3XAF7QSRCUB/action/citation_signature","submit_replication":"https://pith.science/pith/LM33NZZMUJX7ROK3XAF7QSRCUB/action/replication_record"}},"created_at":"2026-05-20T00:04:23.586156+00:00","updated_at":"2026-05-20T00:04:23.586156+00:00"}