{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:A2OJRPTJ67DIYMPXCKZGUHFEPP","short_pith_number":"pith:A2OJRPTJ","schema_version":"1.0","canonical_sha256":"069c98be69f7c68c31f712b26a1ca47bd4e0c4280bf13f5067f64ff4270f48b4","source":{"kind":"arxiv","id":"1402.7107","version":1},"attestation_state":"computed","paper":{"title":"Compressible Generalized Hybrid Monte Carlo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"physics.comp-ph","authors_text":"Jesus-Maria Sanz-Serna, Robert D. Skeel, Youhan Fang","submitted_at":"2014-02-28T00:01:07Z","abstract_excerpt":"One of the most demanding calculations is to generate random samples from a specified probability distribution (usually with an unknown normalizing prefactor) in a high-dimensional configuration space. One often has to resort to using a Markov chain Monte Carlo method, which converges only in the limit to the prescribed distribution. Such methods typically inch through configuration space step by step, with acceptance of a step based on a Metropolis(-Hastings) criterion. An acceptance rate of 100% is possible in principle by embedding configuration space in a higher-dimensional phase space and"},"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":"1402.7107","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.comp-ph","submitted_at":"2014-02-28T00:01:07Z","cross_cats_sorted":["stat.CO"],"title_canon_sha256":"9be005a755550c3d1158d74eb0aeba27b8ea50a060e6825e59d450645aeb0f00","abstract_canon_sha256":"e2e84f6f51390624f708244d4cefc4085fc167beb9b4e6bfcdacb7802c2d86b9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:44:21.958229Z","signature_b64":"zAFBCl1Qlp7u+nJY2f7fHDopt2cCW/aEJbaN7QtZJH9Eh5xaUNc16VEcXIO3LsmpWqWPa3ay4srh7BEb+zJ3Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"069c98be69f7c68c31f712b26a1ca47bd4e0c4280bf13f5067f64ff4270f48b4","last_reissued_at":"2026-05-18T01:44:21.957554Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:44:21.957554Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Compressible Generalized Hybrid Monte Carlo","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO"],"primary_cat":"physics.comp-ph","authors_text":"Jesus-Maria Sanz-Serna, Robert D. Skeel, Youhan Fang","submitted_at":"2014-02-28T00:01:07Z","abstract_excerpt":"One of the most demanding calculations is to generate random samples from a specified probability distribution (usually with an unknown normalizing prefactor) in a high-dimensional configuration space. One often has to resort to using a Markov chain Monte Carlo method, which converges only in the limit to the prescribed distribution. Such methods typically inch through configuration space step by step, with acceptance of a step based on a Metropolis(-Hastings) criterion. An acceptance rate of 100% is possible in principle by embedding configuration space in a higher-dimensional phase space and"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1402.7107","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1402.7107","created_at":"2026-05-18T01:44:21.957658+00:00"},{"alias_kind":"arxiv_version","alias_value":"1402.7107v1","created_at":"2026-05-18T01:44:21.957658+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1402.7107","created_at":"2026-05-18T01:44:21.957658+00:00"},{"alias_kind":"pith_short_12","alias_value":"A2OJRPTJ67DI","created_at":"2026-05-18T12:28:19.803747+00:00"},{"alias_kind":"pith_short_16","alias_value":"A2OJRPTJ67DIYMPX","created_at":"2026-05-18T12:28:19.803747+00:00"},{"alias_kind":"pith_short_8","alias_value":"A2OJRPTJ","created_at":"2026-05-18T12:28:19.803747+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/A2OJRPTJ67DIYMPXCKZGUHFEPP","json":"https://pith.science/pith/A2OJRPTJ67DIYMPXCKZGUHFEPP.json","graph_json":"https://pith.science/api/pith-number/A2OJRPTJ67DIYMPXCKZGUHFEPP/graph.json","events_json":"https://pith.science/api/pith-number/A2OJRPTJ67DIYMPXCKZGUHFEPP/events.json","paper":"https://pith.science/paper/A2OJRPTJ"},"agent_actions":{"view_html":"https://pith.science/pith/A2OJRPTJ67DIYMPXCKZGUHFEPP","download_json":"https://pith.science/pith/A2OJRPTJ67DIYMPXCKZGUHFEPP.json","view_paper":"https://pith.science/paper/A2OJRPTJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1402.7107&json=true","fetch_graph":"https://pith.science/api/pith-number/A2OJRPTJ67DIYMPXCKZGUHFEPP/graph.json","fetch_events":"https://pith.science/api/pith-number/A2OJRPTJ67DIYMPXCKZGUHFEPP/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/A2OJRPTJ67DIYMPXCKZGUHFEPP/action/timestamp_anchor","attest_storage":"https://pith.science/pith/A2OJRPTJ67DIYMPXCKZGUHFEPP/action/storage_attestation","attest_author":"https://pith.science/pith/A2OJRPTJ67DIYMPXCKZGUHFEPP/action/author_attestation","sign_citation":"https://pith.science/pith/A2OJRPTJ67DIYMPXCKZGUHFEPP/action/citation_signature","submit_replication":"https://pith.science/pith/A2OJRPTJ67DIYMPXCKZGUHFEPP/action/replication_record"}},"created_at":"2026-05-18T01:44:21.957658+00:00","updated_at":"2026-05-18T01:44:21.957658+00:00"}