{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:MZWXYEP6Q3MSK5QPLFJA2Z3QGX","short_pith_number":"pith:MZWXYEP6","schema_version":"1.0","canonical_sha256":"666d7c11fe86d925760f59520d677035efbd70a2379023cec5bf12ea7e475a2f","source":{"kind":"arxiv","id":"1608.04484","version":2},"attestation_state":"computed","paper":{"title":"Layered Synthesis of Latent Gaussian Trees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Ali Moharrer, George T. Amariucai, Jing Deng, Shuangqing Wei","submitted_at":"2016-08-16T05:04:30Z","abstract_excerpt":"A new synthesis scheme is proposed to generate a random vector with prescribed joint density that induces a (latent) Gaussian tree structure. The quality of synthesis is shown by vanishing total variation distance between the synthesized and desired statistics. The proposed layered and successive synthesis scheme relies on the learned structure of tree to use sufficient number of common random variables to synthesize the desired density. We characterize the achievable rate region for the rate tuples of multi-layer latent Gaussian tree, through which the number of bits needed to synthesize such"},"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":"1608.04484","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2016-08-16T05:04:30Z","cross_cats_sorted":["math.IT"],"title_canon_sha256":"0c3ab7ee77d9482f0e853e641d573279dffec9f7b82309f607b2bdef73628b48","abstract_canon_sha256":"e92d7b9b6e48366015fcf55214b99a224be940ab964d81450658d3f13f390027"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:44:57.830737Z","signature_b64":"E+kmx4WUsi+ECbhVci8FGUpvjLWOxKJ9xMWxnFfL9N6KycKTjOMY5N86uh1wseF6HRi20DoTTRDFaxWvpM9KCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"666d7c11fe86d925760f59520d677035efbd70a2379023cec5bf12ea7e475a2f","last_reissued_at":"2026-05-18T00:44:57.830354Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:44:57.830354Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Layered Synthesis of Latent Gaussian Trees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.IT"],"primary_cat":"cs.IT","authors_text":"Ali Moharrer, George T. Amariucai, Jing Deng, Shuangqing Wei","submitted_at":"2016-08-16T05:04:30Z","abstract_excerpt":"A new synthesis scheme is proposed to generate a random vector with prescribed joint density that induces a (latent) Gaussian tree structure. The quality of synthesis is shown by vanishing total variation distance between the synthesized and desired statistics. The proposed layered and successive synthesis scheme relies on the learned structure of tree to use sufficient number of common random variables to synthesize the desired density. We characterize the achievable rate region for the rate tuples of multi-layer latent Gaussian tree, through which the number of bits needed to synthesize such"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.04484","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":"1608.04484","created_at":"2026-05-18T00:44:57.830416+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.04484v2","created_at":"2026-05-18T00:44:57.830416+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.04484","created_at":"2026-05-18T00:44:57.830416+00:00"},{"alias_kind":"pith_short_12","alias_value":"MZWXYEP6Q3MS","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_16","alias_value":"MZWXYEP6Q3MSK5QP","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_8","alias_value":"MZWXYEP6","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/MZWXYEP6Q3MSK5QPLFJA2Z3QGX","json":"https://pith.science/pith/MZWXYEP6Q3MSK5QPLFJA2Z3QGX.json","graph_json":"https://pith.science/api/pith-number/MZWXYEP6Q3MSK5QPLFJA2Z3QGX/graph.json","events_json":"https://pith.science/api/pith-number/MZWXYEP6Q3MSK5QPLFJA2Z3QGX/events.json","paper":"https://pith.science/paper/MZWXYEP6"},"agent_actions":{"view_html":"https://pith.science/pith/MZWXYEP6Q3MSK5QPLFJA2Z3QGX","download_json":"https://pith.science/pith/MZWXYEP6Q3MSK5QPLFJA2Z3QGX.json","view_paper":"https://pith.science/paper/MZWXYEP6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.04484&json=true","fetch_graph":"https://pith.science/api/pith-number/MZWXYEP6Q3MSK5QPLFJA2Z3QGX/graph.json","fetch_events":"https://pith.science/api/pith-number/MZWXYEP6Q3MSK5QPLFJA2Z3QGX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MZWXYEP6Q3MSK5QPLFJA2Z3QGX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MZWXYEP6Q3MSK5QPLFJA2Z3QGX/action/storage_attestation","attest_author":"https://pith.science/pith/MZWXYEP6Q3MSK5QPLFJA2Z3QGX/action/author_attestation","sign_citation":"https://pith.science/pith/MZWXYEP6Q3MSK5QPLFJA2Z3QGX/action/citation_signature","submit_replication":"https://pith.science/pith/MZWXYEP6Q3MSK5QPLFJA2Z3QGX/action/replication_record"}},"created_at":"2026-05-18T00:44:57.830416+00:00","updated_at":"2026-05-18T00:44:57.830416+00:00"}