{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:OXNRIP72O7TS7OZGQWDKHPMTTN","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":"f522f4bd0351ff0240847b5abd69d64b9fd28b2cc4b2d6c57c70efeee79a652f","cross_cats_sorted":["cs.LG","stat.CO","stat.ME","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2015-05-29T05:33:22Z","title_canon_sha256":"756d2c14d86f97e1ecf3972ddf651d92dd39cdab454640583cd0217d7062a654"},"schema_version":"1.0","source":{"id":"1505.07925","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1505.07925","created_at":"2026-05-18T02:00:00Z"},{"alias_kind":"arxiv_version","alias_value":"1505.07925v1","created_at":"2026-05-18T02:00:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1505.07925","created_at":"2026-05-18T02:00:00Z"},{"alias_kind":"pith_short_12","alias_value":"OXNRIP72O7TS","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_16","alias_value":"OXNRIP72O7TS7OZG","created_at":"2026-05-18T12:29:34Z"},{"alias_kind":"pith_short_8","alias_value":"OXNRIP72","created_at":"2026-05-18T12:29:34Z"}],"graph_snapshots":[{"event_id":"sha256:dfa72b9ccbb217dd3745be20ca38cebde2410df816bda5c8e04d39bee4367c1b","target":"graph","created_at":"2026-05-18T02:00:00Z","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":"We study the computational complexity of Markov chain Monte Carlo (MCMC) methods for high-dimensional Bayesian linear regression under sparsity constraints. We first show that a Bayesian approach can achieve variable-selection consistency under relatively mild conditions on the design matrix. We then demonstrate that the statistical criterion of posterior concentration need not imply the computational desideratum of rapid mixing of the MCMC algorithm. By introducing a truncated sparsity prior for variable selection, we provide a set of conditions that guarantee both variable-selection consiste","authors_text":"Martin J. Wainwright, Michael I. Jordan, Yun Yang","cross_cats":["cs.LG","stat.CO","stat.ME","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2015-05-29T05:33:22Z","title":"On the Computational Complexity of High-Dimensional Bayesian Variable Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1505.07925","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":"sha256:8dc2b008a2a887f69500be532051790a29e0f9009da2e24e94c5e2c550f515e9","target":"record","created_at":"2026-05-18T02:00:00Z","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":"f522f4bd0351ff0240847b5abd69d64b9fd28b2cc4b2d6c57c70efeee79a652f","cross_cats_sorted":["cs.LG","stat.CO","stat.ME","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2015-05-29T05:33:22Z","title_canon_sha256":"756d2c14d86f97e1ecf3972ddf651d92dd39cdab454640583cd0217d7062a654"},"schema_version":"1.0","source":{"id":"1505.07925","kind":"arxiv","version":1}},"canonical_sha256":"75db143ffa77e72fbb268586a3bd939b473060a0e472f839f252f0b6da4b7934","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"75db143ffa77e72fbb268586a3bd939b473060a0e472f839f252f0b6da4b7934","first_computed_at":"2026-05-18T02:00:00.294280Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:00:00.294280Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"a6T9HRk6tYZRIZY3Lvomaz0mBkrkZ4Hk6zowg+O3AReZv5siBQ+3F0/z7D6NtEpLbbfOLPFaLDlEkOwnmDuyCA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:00:00.294813Z","signed_message":"canonical_sha256_bytes"},"source_id":"1505.07925","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8dc2b008a2a887f69500be532051790a29e0f9009da2e24e94c5e2c550f515e9","sha256:dfa72b9ccbb217dd3745be20ca38cebde2410df816bda5c8e04d39bee4367c1b"],"state_sha256":"91acc2e2ac83c071951f2a5640f12c8ebaaf97e07fc333b9c6c55f504bf9d222"}