{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:NNFZJNZYF7DQUXQS3UX6NYUCNF","short_pith_number":"pith:NNFZJNZY","schema_version":"1.0","canonical_sha256":"6b4b94b7382fc70a5e12dd2fe6e282697eea456e287ec0a70c11ed421191f2b4","source":{"kind":"arxiv","id":"1602.04060","version":2},"attestation_state":"computed","paper":{"title":"Discrete approximation of a mixture distribution via restricted divergence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Christian R\\\"over, Tim Friede","submitted_at":"2016-02-12T14:08:42Z","abstract_excerpt":"Mixture distributions arise in many application areas, for example as marginal distributions or convolutions of distributions. We present a method of constructing an easily tractable discrete mixture distribution as an approximation to a mixture distribution with a large to infinite number, discrete or continuous, of components. The proposed DIRECT (Divergence Restricting Conditional Tesselation) algorithm is set up such that a pre-specified precision, defined in terms of Kullback-Leibler divergence between true distribution and approximation, is guaranteed. Application of the algorithm is dem"},"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":"1602.04060","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2016-02-12T14:08:42Z","cross_cats_sorted":[],"title_canon_sha256":"ea28afb95c9956eeb3632a2ddf6d4d16c6b2f71b07ecacd4f5877f77be04f6f3","abstract_canon_sha256":"bd7a31f16e5171cd0ba4eeb8073e49db43611a2b0041f0e7fb75ee613907abf3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:50:35.783223Z","signature_b64":"na6dAzqj1itUAaZF3lq7ka1ptGB0tdEmyRtlKwUj3Je49/bs9gpbHJC0l9Yxh8HmU7nNieRR84ltNr21VtHfAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6b4b94b7382fc70a5e12dd2fe6e282697eea456e287ec0a70c11ed421191f2b4","last_reissued_at":"2026-05-18T00:50:35.782468Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:50:35.782468Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Discrete approximation of a mixture distribution via restricted divergence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Christian R\\\"over, Tim Friede","submitted_at":"2016-02-12T14:08:42Z","abstract_excerpt":"Mixture distributions arise in many application areas, for example as marginal distributions or convolutions of distributions. We present a method of constructing an easily tractable discrete mixture distribution as an approximation to a mixture distribution with a large to infinite number, discrete or continuous, of components. The proposed DIRECT (Divergence Restricting Conditional Tesselation) algorithm is set up such that a pre-specified precision, defined in terms of Kullback-Leibler divergence between true distribution and approximation, is guaranteed. Application of the algorithm is dem"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1602.04060","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1602.04060","created_at":"2026-05-18T00:50:35.782590+00:00"},{"alias_kind":"arxiv_version","alias_value":"1602.04060v2","created_at":"2026-05-18T00:50:35.782590+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1602.04060","created_at":"2026-05-18T00:50:35.782590+00:00"},{"alias_kind":"pith_short_12","alias_value":"NNFZJNZYF7DQ","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_16","alias_value":"NNFZJNZYF7DQUXQS","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_8","alias_value":"NNFZJNZY","created_at":"2026-05-18T12:30:32.724797+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/NNFZJNZYF7DQUXQS3UX6NYUCNF","json":"https://pith.science/pith/NNFZJNZYF7DQUXQS3UX6NYUCNF.json","graph_json":"https://pith.science/api/pith-number/NNFZJNZYF7DQUXQS3UX6NYUCNF/graph.json","events_json":"https://pith.science/api/pith-number/NNFZJNZYF7DQUXQS3UX6NYUCNF/events.json","paper":"https://pith.science/paper/NNFZJNZY"},"agent_actions":{"view_html":"https://pith.science/pith/NNFZJNZYF7DQUXQS3UX6NYUCNF","download_json":"https://pith.science/pith/NNFZJNZYF7DQUXQS3UX6NYUCNF.json","view_paper":"https://pith.science/paper/NNFZJNZY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1602.04060&json=true","fetch_graph":"https://pith.science/api/pith-number/NNFZJNZYF7DQUXQS3UX6NYUCNF/graph.json","fetch_events":"https://pith.science/api/pith-number/NNFZJNZYF7DQUXQS3UX6NYUCNF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NNFZJNZYF7DQUXQS3UX6NYUCNF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NNFZJNZYF7DQUXQS3UX6NYUCNF/action/storage_attestation","attest_author":"https://pith.science/pith/NNFZJNZYF7DQUXQS3UX6NYUCNF/action/author_attestation","sign_citation":"https://pith.science/pith/NNFZJNZYF7DQUXQS3UX6NYUCNF/action/citation_signature","submit_replication":"https://pith.science/pith/NNFZJNZYF7DQUXQS3UX6NYUCNF/action/replication_record"}},"created_at":"2026-05-18T00:50:35.782590+00:00","updated_at":"2026-05-18T00:50:35.782590+00:00"}