{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:MLTY66JZNR2CJFSNSAYC2FBCJA","short_pith_number":"pith:MLTY66JZ","canonical_record":{"source":{"id":"1603.05031","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-03-16T11:01:56Z","cross_cats_sorted":[],"title_canon_sha256":"17006bb2f6734b33cf1939bdc597a8b819e9da114e40055ee85c8e954778649a","abstract_canon_sha256":"6634a0fca84f316c6d7ddc3a46daac1c867503f5f1e10968c00d946e758b8eb8"},"schema_version":"1.0"},"canonical_sha256":"62e78f79396c7424964d90302d142248003a07fac4618caf81dd2732f96fb838","source":{"kind":"arxiv","id":"1603.05031","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.05031","created_at":"2026-05-17T23:59:33Z"},{"alias_kind":"arxiv_version","alias_value":"1603.05031v3","created_at":"2026-05-17T23:59:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.05031","created_at":"2026-05-17T23:59:33Z"},{"alias_kind":"pith_short_12","alias_value":"MLTY66JZNR2C","created_at":"2026-05-18T12:30:32Z"},{"alias_kind":"pith_short_16","alias_value":"MLTY66JZNR2CJFSN","created_at":"2026-05-18T12:30:32Z"},{"alias_kind":"pith_short_8","alias_value":"MLTY66JZ","created_at":"2026-05-18T12:30:32Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:MLTY66JZNR2CJFSNSAYC2FBCJA","target":"record","payload":{"canonical_record":{"source":{"id":"1603.05031","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-03-16T11:01:56Z","cross_cats_sorted":[],"title_canon_sha256":"17006bb2f6734b33cf1939bdc597a8b819e9da114e40055ee85c8e954778649a","abstract_canon_sha256":"6634a0fca84f316c6d7ddc3a46daac1c867503f5f1e10968c00d946e758b8eb8"},"schema_version":"1.0"},"canonical_sha256":"62e78f79396c7424964d90302d142248003a07fac4618caf81dd2732f96fb838","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:33.480436Z","signature_b64":"5QenrjQKeCOuuMLp4xQL/kuT0zT9l1jtVaaVW7l9FiiEYX7tMVFz5Nzci3/N9djg5w6hVr88+HSBTpavKBdUBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"62e78f79396c7424964d90302d142248003a07fac4618caf81dd2732f96fb838","last_reissued_at":"2026-05-17T23:59:33.479741Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:33.479741Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1603.05031","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:59:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9O324YxOUfiiOL1lXbSBSktv072Y0tlQbBKJ8YGn8BH0YBd0xfjGeH8xDd1aaB5KkxWKRr/RCeLSUuFqQ5YKDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T14:52:01.923165Z"},"content_sha256":"756cadc8fabf176cfc9b7f6327df8ae6ebe94e7e16cbaa6b2c381ce19460acbe","schema_version":"1.0","event_id":"sha256:756cadc8fabf176cfc9b7f6327df8ae6ebe94e7e16cbaa6b2c381ce19460acbe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:MLTY66JZNR2CJFSNSAYC2FBCJA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Estimating orthant probabilities of high dimensional Gaussian vectors with an application to set estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Dario Azzimonti (Idiap, David Ginsbourger (Idiap, IMSV)","submitted_at":"2016-03-16T11:01:56Z","abstract_excerpt":"The computation of Gaussian orthant probabilities has been extensively studied for low-dimensional vectors. Here, we focus on the high-dimensional case and we present a two-step procedure relying on both deterministic and stochastic techniques. The proposed estimator relies indeed on splitting the probability into a low-dimensional term and a remainder. While the low-dimensional probability can be estimated by fast and accurate quadrature, the remainder requires Monte Carlo sampling. We further refine the estimation by using a novel asymmetric nested Monte Carlo (anMC) algorithm for the remain"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.05031","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":""},"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":"2026-05-17T23:59:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pcAVmNZd0MZvfQ9AYflIjTIP4Se4LOcuc5JvNiQkJUUDb5/4T/9RhpyTPywuWmSH22cEv09CMnHNr4eWjGDvBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-23T14:52:01.923535Z"},"content_sha256":"02a8af1046bbac24130b4377f438f4c711c1f45290a3fead0fbc6fc7bd95d078","schema_version":"1.0","event_id":"sha256:02a8af1046bbac24130b4377f438f4c711c1f45290a3fead0fbc6fc7bd95d078"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MLTY66JZNR2CJFSNSAYC2FBCJA/bundle.json","state_url":"https://pith.science/pith/MLTY66JZNR2CJFSNSAYC2FBCJA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MLTY66JZNR2CJFSNSAYC2FBCJA/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":"2026-06-23T14:52:01Z","links":{"resolver":"https://pith.science/pith/MLTY66JZNR2CJFSNSAYC2FBCJA","bundle":"https://pith.science/pith/MLTY66JZNR2CJFSNSAYC2FBCJA/bundle.json","state":"https://pith.science/pith/MLTY66JZNR2CJFSNSAYC2FBCJA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MLTY66JZNR2CJFSNSAYC2FBCJA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:MLTY66JZNR2CJFSNSAYC2FBCJA","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":"6634a0fca84f316c6d7ddc3a46daac1c867503f5f1e10968c00d946e758b8eb8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-03-16T11:01:56Z","title_canon_sha256":"17006bb2f6734b33cf1939bdc597a8b819e9da114e40055ee85c8e954778649a"},"schema_version":"1.0","source":{"id":"1603.05031","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.05031","created_at":"2026-05-17T23:59:33Z"},{"alias_kind":"arxiv_version","alias_value":"1603.05031v3","created_at":"2026-05-17T23:59:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.05031","created_at":"2026-05-17T23:59:33Z"},{"alias_kind":"pith_short_12","alias_value":"MLTY66JZNR2C","created_at":"2026-05-18T12:30:32Z"},{"alias_kind":"pith_short_16","alias_value":"MLTY66JZNR2CJFSN","created_at":"2026-05-18T12:30:32Z"},{"alias_kind":"pith_short_8","alias_value":"MLTY66JZ","created_at":"2026-05-18T12:30:32Z"}],"graph_snapshots":[{"event_id":"sha256:02a8af1046bbac24130b4377f438f4c711c1f45290a3fead0fbc6fc7bd95d078","target":"graph","created_at":"2026-05-17T23:59:33Z","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":"The computation of Gaussian orthant probabilities has been extensively studied for low-dimensional vectors. Here, we focus on the high-dimensional case and we present a two-step procedure relying on both deterministic and stochastic techniques. The proposed estimator relies indeed on splitting the probability into a low-dimensional term and a remainder. While the low-dimensional probability can be estimated by fast and accurate quadrature, the remainder requires Monte Carlo sampling. We further refine the estimation by using a novel asymmetric nested Monte Carlo (anMC) algorithm for the remain","authors_text":"Dario Azzimonti (Idiap, David Ginsbourger (Idiap, IMSV)","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-03-16T11:01:56Z","title":"Estimating orthant probabilities of high dimensional Gaussian vectors with an application to set estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.05031","kind":"arxiv","version":3},"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":"sha256:756cadc8fabf176cfc9b7f6327df8ae6ebe94e7e16cbaa6b2c381ce19460acbe","target":"record","created_at":"2026-05-17T23:59:33Z","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":"6634a0fca84f316c6d7ddc3a46daac1c867503f5f1e10968c00d946e758b8eb8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-03-16T11:01:56Z","title_canon_sha256":"17006bb2f6734b33cf1939bdc597a8b819e9da114e40055ee85c8e954778649a"},"schema_version":"1.0","source":{"id":"1603.05031","kind":"arxiv","version":3}},"canonical_sha256":"62e78f79396c7424964d90302d142248003a07fac4618caf81dd2732f96fb838","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"62e78f79396c7424964d90302d142248003a07fac4618caf81dd2732f96fb838","first_computed_at":"2026-05-17T23:59:33.479741Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:59:33.479741Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5QenrjQKeCOuuMLp4xQL/kuT0zT9l1jtVaaVW7l9FiiEYX7tMVFz5Nzci3/N9djg5w6hVr88+HSBTpavKBdUBA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:59:33.480436Z","signed_message":"canonical_sha256_bytes"},"source_id":"1603.05031","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:756cadc8fabf176cfc9b7f6327df8ae6ebe94e7e16cbaa6b2c381ce19460acbe","sha256:02a8af1046bbac24130b4377f438f4c711c1f45290a3fead0fbc6fc7bd95d078"],"state_sha256":"57e9aa1b7107cc281336e7be0c25f9c8f4bc39c4ced1d38fd7b4b9bc6642b6dc"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LYMpfQ4+5Kc7flVp3Fn94p9Uj+XKAthggpbgnI8jIB/B7Vxh9unJo/fGp7eHzH7rrKKd/KH2v7lt15Y0sdWhDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-23T14:52:01.925501Z","bundle_sha256":"5b381f3552462d9e9fd5e35e5ae16e136a2b8705b51c86f14d704ff9a9b55b95"}}