{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:QSHMY4HU2XDWFEH4UDVPHNSS54","short_pith_number":"pith:QSHMY4HU","canonical_record":{"source":{"id":"1710.03500","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2017-10-10T10:29:26Z","cross_cats_sorted":[],"title_canon_sha256":"20508881d6eae79a8be629d35df583488f12b93fafdc09b533e64da77f77e7f0","abstract_canon_sha256":"8705655117c4fcec85b9e09c4bf0995240598d65498dd1c453a032ec4feff7fc"},"schema_version":"1.0"},"canonical_sha256":"848ecc70f4d5c76290fca0eaf3b652ef39c45741b371d39e5c15c8144871c91a","source":{"kind":"arxiv","id":"1710.03500","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.03500","created_at":"2026-05-18T00:19:26Z"},{"alias_kind":"arxiv_version","alias_value":"1710.03500v1","created_at":"2026-05-18T00:19:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.03500","created_at":"2026-05-18T00:19:26Z"},{"alias_kind":"pith_short_12","alias_value":"QSHMY4HU2XDW","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QSHMY4HU2XDWFEH4","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QSHMY4HU","created_at":"2026-05-18T12:31:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:QSHMY4HU2XDWFEH4UDVPHNSS54","target":"record","payload":{"canonical_record":{"source":{"id":"1710.03500","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2017-10-10T10:29:26Z","cross_cats_sorted":[],"title_canon_sha256":"20508881d6eae79a8be629d35df583488f12b93fafdc09b533e64da77f77e7f0","abstract_canon_sha256":"8705655117c4fcec85b9e09c4bf0995240598d65498dd1c453a032ec4feff7fc"},"schema_version":"1.0"},"canonical_sha256":"848ecc70f4d5c76290fca0eaf3b652ef39c45741b371d39e5c15c8144871c91a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:19:26.316383Z","signature_b64":"eAsppoJp/XUdg2acI1/KhAJ3Olcf7gWCNzA2vS55sXkKuQa95njrR/RIi96V7lvEfZ4FKP9aay21rFzuoYHaAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"848ecc70f4d5c76290fca0eaf3b652ef39c45741b371d39e5c15c8144871c91a","last_reissued_at":"2026-05-18T00:19:26.315945Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:19:26.315945Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.03500","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:19:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"heWL2f0p6AzDy8DE1MojaLqk/zKvWCsOp/7QdV1CktabWPTQMgMROFXOGxDS63zKDoMVdBDd2CTlXubY5wryDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:17:31.376514Z"},"content_sha256":"1e440ea0cdd3c282d9ffb00dcbe4884e2da3b517b122307ecf982b33f614a232","schema_version":"1.0","event_id":"sha256:1e440ea0cdd3c282d9ffb00dcbe4884e2da3b517b122307ecf982b33f614a232"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:QSHMY4HU2XDWFEH4UDVPHNSS54","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"math.NA","authors_text":"Ben Mansour Dia, Joakim Beck, Luis FR Espath, Quan Long, Raul Tempone","submitted_at":"2017-10-10T10:29:26Z","abstract_excerpt":"In calculating expected information gain in optimal Bayesian experimental design, the computation of the inner loop in the classical double-loop Monte Carlo requires a large number of samples and suffers from underflow if the number of samples is small. These drawbacks can be avoided by using an importance sampling approach. We present a computationally efficient method for optimal Bayesian experimental design that introduces importance sampling based on the Laplace method to the inner loop. We derive the optimal values for the method parameters in which the average computational cost is minim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.03500","kind":"arxiv","version":1},"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":"2026-05-18T00:19:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"wzc1a2ldwfmhAjJZsf4J2lfPzTcTohERHijmi04roEuWarBbimzE8B5ejRXc6fuZ3zub4t6TdmSuILRWU/pzDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T11:17:31.377227Z"},"content_sha256":"0fc663e381e9b6fd963f142942d44f00183cf6699d702e34a4e6a38aa084c90b","schema_version":"1.0","event_id":"sha256:0fc663e381e9b6fd963f142942d44f00183cf6699d702e34a4e6a38aa084c90b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QSHMY4HU2XDWFEH4UDVPHNSS54/bundle.json","state_url":"https://pith.science/pith/QSHMY4HU2XDWFEH4UDVPHNSS54/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QSHMY4HU2XDWFEH4UDVPHNSS54/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":"2026-05-27T11:17:31Z","links":{"resolver":"https://pith.science/pith/QSHMY4HU2XDWFEH4UDVPHNSS54","bundle":"https://pith.science/pith/QSHMY4HU2XDWFEH4UDVPHNSS54/bundle.json","state":"https://pith.science/pith/QSHMY4HU2XDWFEH4UDVPHNSS54/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QSHMY4HU2XDWFEH4UDVPHNSS54/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:QSHMY4HU2XDWFEH4UDVPHNSS54","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":"8705655117c4fcec85b9e09c4bf0995240598d65498dd1c453a032ec4feff7fc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2017-10-10T10:29:26Z","title_canon_sha256":"20508881d6eae79a8be629d35df583488f12b93fafdc09b533e64da77f77e7f0"},"schema_version":"1.0","source":{"id":"1710.03500","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.03500","created_at":"2026-05-18T00:19:26Z"},{"alias_kind":"arxiv_version","alias_value":"1710.03500v1","created_at":"2026-05-18T00:19:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.03500","created_at":"2026-05-18T00:19:26Z"},{"alias_kind":"pith_short_12","alias_value":"QSHMY4HU2XDW","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_16","alias_value":"QSHMY4HU2XDWFEH4","created_at":"2026-05-18T12:31:39Z"},{"alias_kind":"pith_short_8","alias_value":"QSHMY4HU","created_at":"2026-05-18T12:31:39Z"}],"graph_snapshots":[{"event_id":"sha256:0fc663e381e9b6fd963f142942d44f00183cf6699d702e34a4e6a38aa084c90b","target":"graph","created_at":"2026-05-18T00:19:26Z","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":"In calculating expected information gain in optimal Bayesian experimental design, the computation of the inner loop in the classical double-loop Monte Carlo requires a large number of samples and suffers from underflow if the number of samples is small. These drawbacks can be avoided by using an importance sampling approach. We present a computationally efficient method for optimal Bayesian experimental design that introduces importance sampling based on the Laplace method to the inner loop. We derive the optimal values for the method parameters in which the average computational cost is minim","authors_text":"Ben Mansour Dia, Joakim Beck, Luis FR Espath, Quan Long, Raul Tempone","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2017-10-10T10:29:26Z","title":"Fast Bayesian experimental design: Laplace-based importance sampling for the expected information gain"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.03500","kind":"arxiv","version":1},"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":"sha256:1e440ea0cdd3c282d9ffb00dcbe4884e2da3b517b122307ecf982b33f614a232","target":"record","created_at":"2026-05-18T00:19:26Z","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":"8705655117c4fcec85b9e09c4bf0995240598d65498dd1c453a032ec4feff7fc","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.NA","submitted_at":"2017-10-10T10:29:26Z","title_canon_sha256":"20508881d6eae79a8be629d35df583488f12b93fafdc09b533e64da77f77e7f0"},"schema_version":"1.0","source":{"id":"1710.03500","kind":"arxiv","version":1}},"canonical_sha256":"848ecc70f4d5c76290fca0eaf3b652ef39c45741b371d39e5c15c8144871c91a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"848ecc70f4d5c76290fca0eaf3b652ef39c45741b371d39e5c15c8144871c91a","first_computed_at":"2026-05-18T00:19:26.315945Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:19:26.315945Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eAsppoJp/XUdg2acI1/KhAJ3Olcf7gWCNzA2vS55sXkKuQa95njrR/RIi96V7lvEfZ4FKP9aay21rFzuoYHaAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:19:26.316383Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.03500","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1e440ea0cdd3c282d9ffb00dcbe4884e2da3b517b122307ecf982b33f614a232","sha256:0fc663e381e9b6fd963f142942d44f00183cf6699d702e34a4e6a38aa084c90b"],"state_sha256":"ddb29b6736edd30068edd74cd1aac140577cffe2f1abea67a25294030097e47d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zqbZ1nRJMXKOCp4STkRGhPY5nGfNaw8ax+LZijWOuKwx0nU+02GVkUNkjn+VzdLr9H2R4gc5t5fZQ/sWEHo7CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T11:17:31.380282Z","bundle_sha256":"ee845a46319316175da1fbf587e825b7ae634e65c42b8305feacaf196050a126"}}