{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:BLTY4H423W6QH3QORP2MGSOE33","short_pith_number":"pith:BLTY4H42","schema_version":"1.0","canonical_sha256":"0ae78e1f9addbd03ee0e8bf4c349c4dedac042f2304277f6670bfc8d74620348","source":{"kind":"arxiv","id":"2006.12871","version":2},"attestation_state":"computed","paper":{"title":"not-MIWAE: Deep Generative Modelling with Missing not at Random Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Jes Frellsen, Niels Bruun Ipsen, Pierre-Alexandre Mattei","submitted_at":"2020-06-23T10:06:21Z","abstract_excerpt":"When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference. We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data. Specifically, a deep neural network enables us to flexibly model the conditional distribution of the missingness pattern given the data. This allows for incorporating prior information about the type of missingness (e.g. self-censoring) into the model. Our inference technique, based "},"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":"2006.12871","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2020-06-23T10:06:21Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"7119a5c1427e7b58a58031af72f59252cd94f629111d46014009c4d3603edc5d","abstract_canon_sha256":"ff1c9ca6130a796a7bb75a58c36ed5dce54e15df78253d82a143b554425132bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:24:10.046809Z","signature_b64":"UEvInAksMRagUAF2OHIfgU6GfmzdqqCx2KZpQOK20zrIQW+NK0Ka+Sq/sG7n5NLZepHXt17cLRJIuQyDiP8wBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0ae78e1f9addbd03ee0e8bf4c349c4dedac042f2304277f6670bfc8d74620348","last_reissued_at":"2026-07-05T02:24:10.046396Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:24:10.046396Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"not-MIWAE: Deep Generative Modelling with Missing not at Random Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Jes Frellsen, Niels Bruun Ipsen, Pierre-Alexandre Mattei","submitted_at":"2020-06-23T10:06:21Z","abstract_excerpt":"When a missing process depends on the missing values themselves, it needs to be explicitly modelled and taken into account while doing likelihood-based inference. We present an approach for building and fitting deep latent variable models (DLVMs) in cases where the missing process is dependent on the missing data. Specifically, a deep neural network enables us to flexibly model the conditional distribution of the missingness pattern given the data. This allows for incorporating prior information about the type of missingness (e.g. self-censoring) into the model. Our inference technique, based "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2006.12871","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2006.12871/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":"2006.12871","created_at":"2026-07-05T02:24:10.046452+00:00"},{"alias_kind":"arxiv_version","alias_value":"2006.12871v2","created_at":"2026-07-05T02:24:10.046452+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2006.12871","created_at":"2026-07-05T02:24:10.046452+00:00"},{"alias_kind":"pith_short_12","alias_value":"BLTY4H423W6Q","created_at":"2026-07-05T02:24:10.046452+00:00"},{"alias_kind":"pith_short_16","alias_value":"BLTY4H423W6QH3QO","created_at":"2026-07-05T02:24:10.046452+00:00"},{"alias_kind":"pith_short_8","alias_value":"BLTY4H42","created_at":"2026-07-05T02:24:10.046452+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25439","citing_title":"Missing Pattern Recognized Diffusion Imputation Model for Missing Not At Random","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2509.20098","citing_title":"Incomplete Data, Complete Dynamics: A Diffusion Approach","ref_index":24,"is_internal_anchor":false},{"citing_arxiv_id":"2602.00844","citing_title":"Multivariate Time Series Data Imputation via Distributionally Robust Regularization","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2605.01676","citing_title":"Missingness-aware Data Imputation via AI-powered Bayesian Generative Modeling","ref_index":2,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BLTY4H423W6QH3QORP2MGSOE33","json":"https://pith.science/pith/BLTY4H423W6QH3QORP2MGSOE33.json","graph_json":"https://pith.science/api/pith-number/BLTY4H423W6QH3QORP2MGSOE33/graph.json","events_json":"https://pith.science/api/pith-number/BLTY4H423W6QH3QORP2MGSOE33/events.json","paper":"https://pith.science/paper/BLTY4H42"},"agent_actions":{"view_html":"https://pith.science/pith/BLTY4H423W6QH3QORP2MGSOE33","download_json":"https://pith.science/pith/BLTY4H423W6QH3QORP2MGSOE33.json","view_paper":"https://pith.science/paper/BLTY4H42","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2006.12871&json=true","fetch_graph":"https://pith.science/api/pith-number/BLTY4H423W6QH3QORP2MGSOE33/graph.json","fetch_events":"https://pith.science/api/pith-number/BLTY4H423W6QH3QORP2MGSOE33/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BLTY4H423W6QH3QORP2MGSOE33/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BLTY4H423W6QH3QORP2MGSOE33/action/storage_attestation","attest_author":"https://pith.science/pith/BLTY4H423W6QH3QORP2MGSOE33/action/author_attestation","sign_citation":"https://pith.science/pith/BLTY4H423W6QH3QORP2MGSOE33/action/citation_signature","submit_replication":"https://pith.science/pith/BLTY4H423W6QH3QORP2MGSOE33/action/replication_record"}},"created_at":"2026-07-05T02:24:10.046452+00:00","updated_at":"2026-07-05T02:24:10.046452+00:00"}