{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:AYZPF4ZWCG2ZCNWOPZRPOJLW2X","short_pith_number":"pith:AYZPF4ZW","canonical_record":{"source":{"id":"1705.00841","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-05-02T08:03:29Z","cross_cats_sorted":[],"title_canon_sha256":"c51fc84066cd7c3feb0fde68862eeb840d4e3ecdaab48b9bbfb3ca838811117b","abstract_canon_sha256":"1fdddc304668af86b7c9a5d0cf27120e2a95f498b5aedbe0c57a33607fee8618"},"schema_version":"1.0"},"canonical_sha256":"0632f2f33611b59136ce7e62f72576d5cb519ed0065f3440159d213c5376d6b5","source":{"kind":"arxiv","id":"1705.00841","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.00841","created_at":"2026-05-18T00:03:25Z"},{"alias_kind":"arxiv_version","alias_value":"1705.00841v3","created_at":"2026-05-18T00:03:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.00841","created_at":"2026-05-18T00:03:25Z"},{"alias_kind":"pith_short_12","alias_value":"AYZPF4ZWCG2Z","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"AYZPF4ZWCG2ZCNWO","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"AYZPF4ZW","created_at":"2026-05-18T12:31:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:AYZPF4ZWCG2ZCNWOPZRPOJLW2X","target":"record","payload":{"canonical_record":{"source":{"id":"1705.00841","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-05-02T08:03:29Z","cross_cats_sorted":[],"title_canon_sha256":"c51fc84066cd7c3feb0fde68862eeb840d4e3ecdaab48b9bbfb3ca838811117b","abstract_canon_sha256":"1fdddc304668af86b7c9a5d0cf27120e2a95f498b5aedbe0c57a33607fee8618"},"schema_version":"1.0"},"canonical_sha256":"0632f2f33611b59136ce7e62f72576d5cb519ed0065f3440159d213c5376d6b5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:03:25.081731Z","signature_b64":"LbRXJTCI8BX/dIqLBkiRliXm3J41XhDyHGRLQXuXVxK9lM1VuaqkZj2pv8ORGn1R+9EqBl7O0WZ2HNPdMmdsDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0632f2f33611b59136ce7e62f72576d5cb519ed0065f3440159d213c5376d6b5","last_reissued_at":"2026-05-18T00:03:25.081322Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:03:25.081322Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1705.00841","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":"2026-05-18T00:03:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"peuD2aLUnTGqKPTDbpnYTaz7NJiAkSXiMEohMl11x4frPXm/XoDPUzgyGSuJ4BfTMm2ESJgedWY+VUdATRgNCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:02:25.139556Z"},"content_sha256":"d97e02febae000c8cb3f425251c7a6691643677be06c03971f28a25c08b21388","schema_version":"1.0","event_id":"sha256:d97e02febae000c8cb3f425251c7a6691643677be06c03971f28a25c08b21388"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:AYZPF4ZWCG2ZCNWOPZRPOJLW2X","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayes Shrinkage at GWAS scale: Convergence and Approximation Theory of a Scalable MCMC Algorithm for the Horseshoe Prior","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Anirban Bhattacharya, James E. Johndrow, Paulo Orenstein","submitted_at":"2017-05-02T08:03:29Z","abstract_excerpt":"The horseshoe prior is frequently employed in Bayesian analysis of high-dimensional models, and has been shown to achieve minimax optimal risk properties when the truth is sparse. While optimization-based algorithms for the extremely popular Lasso and elastic net procedures can scale to dimension in the hundreds of thousands, algorithms for the horseshoe that use Markov chain Monte Carlo (MCMC) for computation are limited to problems an order of magnitude smaller. This is due to high computational cost per step and growth of the variance of time-averaging estimators as a function of dimension."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.00841","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":"2026-05-18T00:03:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JYK6xPzHdEuDLbk4Z09dgaFl+bXSXqyQ0P4iojqtbOBNHCIpPEnPe2sa1UeayXOH+R+IcsDCigsimamyC0xaBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:02:25.140201Z"},"content_sha256":"a112691f3f55f9249921fd4f853f61af150582868dd02e411a6586191f599a74","schema_version":"1.0","event_id":"sha256:a112691f3f55f9249921fd4f853f61af150582868dd02e411a6586191f599a74"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/AYZPF4ZWCG2ZCNWOPZRPOJLW2X/bundle.json","state_url":"https://pith.science/pith/AYZPF4ZWCG2ZCNWOPZRPOJLW2X/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/AYZPF4ZWCG2ZCNWOPZRPOJLW2X/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":"2026-05-25T19:02:25Z","links":{"resolver":"https://pith.science/pith/AYZPF4ZWCG2ZCNWOPZRPOJLW2X","bundle":"https://pith.science/pith/AYZPF4ZWCG2ZCNWOPZRPOJLW2X/bundle.json","state":"https://pith.science/pith/AYZPF4ZWCG2ZCNWOPZRPOJLW2X/state.json","well_known_bundle":"https://pith.science/.well-known/pith/AYZPF4ZWCG2ZCNWOPZRPOJLW2X/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:AYZPF4ZWCG2ZCNWOPZRPOJLW2X","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":"1fdddc304668af86b7c9a5d0cf27120e2a95f498b5aedbe0c57a33607fee8618","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-05-02T08:03:29Z","title_canon_sha256":"c51fc84066cd7c3feb0fde68862eeb840d4e3ecdaab48b9bbfb3ca838811117b"},"schema_version":"1.0","source":{"id":"1705.00841","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1705.00841","created_at":"2026-05-18T00:03:25Z"},{"alias_kind":"arxiv_version","alias_value":"1705.00841v3","created_at":"2026-05-18T00:03:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1705.00841","created_at":"2026-05-18T00:03:25Z"},{"alias_kind":"pith_short_12","alias_value":"AYZPF4ZWCG2Z","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_16","alias_value":"AYZPF4ZWCG2ZCNWO","created_at":"2026-05-18T12:31:08Z"},{"alias_kind":"pith_short_8","alias_value":"AYZPF4ZW","created_at":"2026-05-18T12:31:08Z"}],"graph_snapshots":[{"event_id":"sha256:a112691f3f55f9249921fd4f853f61af150582868dd02e411a6586191f599a74","target":"graph","created_at":"2026-05-18T00:03:25Z","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 horseshoe prior is frequently employed in Bayesian analysis of high-dimensional models, and has been shown to achieve minimax optimal risk properties when the truth is sparse. While optimization-based algorithms for the extremely popular Lasso and elastic net procedures can scale to dimension in the hundreds of thousands, algorithms for the horseshoe that use Markov chain Monte Carlo (MCMC) for computation are limited to problems an order of magnitude smaller. This is due to high computational cost per step and growth of the variance of time-averaging estimators as a function of dimension.","authors_text":"Anirban Bhattacharya, James E. Johndrow, Paulo Orenstein","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-05-02T08:03:29Z","title":"Bayes Shrinkage at GWAS scale: Convergence and Approximation Theory of a Scalable MCMC Algorithm for the Horseshoe Prior"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1705.00841","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:d97e02febae000c8cb3f425251c7a6691643677be06c03971f28a25c08b21388","target":"record","created_at":"2026-05-18T00:03:25Z","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":"1fdddc304668af86b7c9a5d0cf27120e2a95f498b5aedbe0c57a33607fee8618","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-05-02T08:03:29Z","title_canon_sha256":"c51fc84066cd7c3feb0fde68862eeb840d4e3ecdaab48b9bbfb3ca838811117b"},"schema_version":"1.0","source":{"id":"1705.00841","kind":"arxiv","version":3}},"canonical_sha256":"0632f2f33611b59136ce7e62f72576d5cb519ed0065f3440159d213c5376d6b5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0632f2f33611b59136ce7e62f72576d5cb519ed0065f3440159d213c5376d6b5","first_computed_at":"2026-05-18T00:03:25.081322Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:03:25.081322Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"LbRXJTCI8BX/dIqLBkiRliXm3J41XhDyHGRLQXuXVxK9lM1VuaqkZj2pv8ORGn1R+9EqBl7O0WZ2HNPdMmdsDg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:03:25.081731Z","signed_message":"canonical_sha256_bytes"},"source_id":"1705.00841","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d97e02febae000c8cb3f425251c7a6691643677be06c03971f28a25c08b21388","sha256:a112691f3f55f9249921fd4f853f61af150582868dd02e411a6586191f599a74"],"state_sha256":"c46cfd6c01616286ea7faa690f66a10f15f3f4113ce2d21d6d106d694d0692ce"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"V3eLreAD1fKuV8Q0pk4X8gwBoODgOUtyN3Y1MpkYaKcY/QgEzg6kI6c5x1oLuPmD4++sGXccPAFEasSVvrlBBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T19:02:25.143650Z","bundle_sha256":"d4591be19c0ae281e5049a3f4e5d6f4ebf6eb5edfc162e99fb4ff0119426db2b"}}