{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:3QV4P2KCCXO3TB73OO5ZBDU56A","short_pith_number":"pith:3QV4P2KC","schema_version":"1.0","canonical_sha256":"dc2bc7e94215ddb987fb73bb908e9df028d7ecda207894c4fa5241b744154590","source":{"kind":"arxiv","id":"2312.07215","version":1},"attestation_state":"computed","paper":{"title":"The energy-stepping Monte Carlo method: an exactly symmetry-preserving, a Hamiltonian Monte Carlo method with a 100% acceptance ratio","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["math.MP","physics.comp-ph"],"primary_cat":"math-ph","authors_text":"Ignacio Romero, Michael Ortiz","submitted_at":"2023-12-12T12:28:36Z","abstract_excerpt":"We introduce the energy-stepping Monte Carlo (ESMC) method, a Markov chain Monte Carlo (MCMC) algorithm based on the conventional dynamical interpretation of the proposal stage but employing an energy-stepping integrator. The energy-stepping integrator is quasi-explicit, symplectic, energy-conserving, and symmetry-preserving. As a result of the exact energy conservation of energy-stepping integrators, ESMC has a 100\\%\\ acceptance ratio of the proposal states. Numerical tests provide empirical evidence that ESMC affords a number of additional benefits: the Markov chains it generates have weak a"},"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":"2312.07215","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"math-ph","submitted_at":"2023-12-12T12:28:36Z","cross_cats_sorted":["math.MP","physics.comp-ph"],"title_canon_sha256":"2a879b77fdbaa34ca51cf15cc23f56056143f5051c95c8ec16877e260d2724c3","abstract_canon_sha256":"a3581fdecf293adb25a6136855ea75a991ff95ad0ec4e133ba38e1a19d76d561"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:23:16.581182Z","signature_b64":"Izx9H4x+LbUkV1REhjnDsaee8bJTII5pzYVwD5eEGP/57S/MchPJsruUqS0an3V+bVQowYgG7N7ZIxSm5hicDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"dc2bc7e94215ddb987fb73bb908e9df028d7ecda207894c4fa5241b744154590","last_reissued_at":"2026-07-05T07:23:16.580687Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:23:16.580687Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The energy-stepping Monte Carlo method: an exactly symmetry-preserving, a Hamiltonian Monte Carlo method with a 100% acceptance ratio","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["math.MP","physics.comp-ph"],"primary_cat":"math-ph","authors_text":"Ignacio Romero, Michael Ortiz","submitted_at":"2023-12-12T12:28:36Z","abstract_excerpt":"We introduce the energy-stepping Monte Carlo (ESMC) method, a Markov chain Monte Carlo (MCMC) algorithm based on the conventional dynamical interpretation of the proposal stage but employing an energy-stepping integrator. The energy-stepping integrator is quasi-explicit, symplectic, energy-conserving, and symmetry-preserving. As a result of the exact energy conservation of energy-stepping integrators, ESMC has a 100\\%\\ acceptance ratio of the proposal states. Numerical tests provide empirical evidence that ESMC affords a number of additional benefits: the Markov chains it generates have weak a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.07215","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2312.07215/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2312.07215","created_at":"2026-07-05T07:23:16.580751+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.07215v1","created_at":"2026-07-05T07:23:16.580751+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.07215","created_at":"2026-07-05T07:23:16.580751+00:00"},{"alias_kind":"pith_short_12","alias_value":"3QV4P2KCCXO3","created_at":"2026-07-05T07:23:16.580751+00:00"},{"alias_kind":"pith_short_16","alias_value":"3QV4P2KCCXO3TB73","created_at":"2026-07-05T07:23:16.580751+00:00"},{"alias_kind":"pith_short_8","alias_value":"3QV4P2KC","created_at":"2026-07-05T07:23:16.580751+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.04895","citing_title":"Posterior sampling in the Age of Emulators","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3QV4P2KCCXO3TB73OO5ZBDU56A","json":"https://pith.science/pith/3QV4P2KCCXO3TB73OO5ZBDU56A.json","graph_json":"https://pith.science/api/pith-number/3QV4P2KCCXO3TB73OO5ZBDU56A/graph.json","events_json":"https://pith.science/api/pith-number/3QV4P2KCCXO3TB73OO5ZBDU56A/events.json","paper":"https://pith.science/paper/3QV4P2KC"},"agent_actions":{"view_html":"https://pith.science/pith/3QV4P2KCCXO3TB73OO5ZBDU56A","download_json":"https://pith.science/pith/3QV4P2KCCXO3TB73OO5ZBDU56A.json","view_paper":"https://pith.science/paper/3QV4P2KC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.07215&json=true","fetch_graph":"https://pith.science/api/pith-number/3QV4P2KCCXO3TB73OO5ZBDU56A/graph.json","fetch_events":"https://pith.science/api/pith-number/3QV4P2KCCXO3TB73OO5ZBDU56A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3QV4P2KCCXO3TB73OO5ZBDU56A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3QV4P2KCCXO3TB73OO5ZBDU56A/action/storage_attestation","attest_author":"https://pith.science/pith/3QV4P2KCCXO3TB73OO5ZBDU56A/action/author_attestation","sign_citation":"https://pith.science/pith/3QV4P2KCCXO3TB73OO5ZBDU56A/action/citation_signature","submit_replication":"https://pith.science/pith/3QV4P2KCCXO3TB73OO5ZBDU56A/action/replication_record"}},"created_at":"2026-07-05T07:23:16.580751+00:00","updated_at":"2026-07-05T07:23:16.580751+00:00"}