{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:3HWXZO3IUCOUVBJR5FSGHPHM6Y","short_pith_number":"pith:3HWXZO3I","canonical_record":{"source":{"id":"1710.04093","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-10-11T14:37:05Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"d624c901f17db97350f0887d8f55212861905d622d7b1db1edc081da05d8772b","abstract_canon_sha256":"be282990ad054703986dacfe7abb0ebd95a906431b4a34e81c7fdb4636dd70c2"},"schema_version":"1.0"},"canonical_sha256":"d9ed7cbb68a09d4a8531e96463bcecf613cce5b38002be3e3bec5aa707706a95","source":{"kind":"arxiv","id":"1710.04093","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.04093","created_at":"2026-05-18T00:32:57Z"},{"alias_kind":"arxiv_version","alias_value":"1710.04093v2","created_at":"2026-05-18T00:32:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04093","created_at":"2026-05-18T00:32:57Z"},{"alias_kind":"pith_short_12","alias_value":"3HWXZO3IUCOU","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"3HWXZO3IUCOUVBJR","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"3HWXZO3I","created_at":"2026-05-18T12:30:58Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:3HWXZO3IUCOUVBJR5FSGHPHM6Y","target":"record","payload":{"canonical_record":{"source":{"id":"1710.04093","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-10-11T14:37:05Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"d624c901f17db97350f0887d8f55212861905d622d7b1db1edc081da05d8772b","abstract_canon_sha256":"be282990ad054703986dacfe7abb0ebd95a906431b4a34e81c7fdb4636dd70c2"},"schema_version":"1.0"},"canonical_sha256":"d9ed7cbb68a09d4a8531e96463bcecf613cce5b38002be3e3bec5aa707706a95","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:32:57.142611Z","signature_b64":"jfQLpNLoxoGSbOWsdSl4wtoIGYBo7dlskdU2XwOkfw6p+jWD2dNnjCjhV7PNYUMLHQBuJAh6rVzESlIe2F0xAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d9ed7cbb68a09d4a8531e96463bcecf613cce5b38002be3e3bec5aa707706a95","last_reissued_at":"2026-05-18T00:32:57.141926Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:32:57.141926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1710.04093","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:32:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sRJeCwV8Xe6Id7JYhRCzi5Nbjq0X+G8aM8PN8VAVueFLzbeiT4fLW1XWXrNXxwoPJ2E0gd+EDnzhvtja78YrBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T22:44:42.803660Z"},"content_sha256":"bd5f69becd1c8422b961aba65739d7e23c2824729dcd0d3b6877272b0f6e3316","schema_version":"1.0","event_id":"sha256:bd5f69becd1c8422b961aba65739d7e23c2824729dcd0d3b6877272b0f6e3316"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:3HWXZO3IUCOUVBJR5FSGHPHM6Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient MCMC for Gibbs Random Fields using pre-computation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.CO","authors_text":"Aidan Boland, Florian Maire, Nial Friel","submitted_at":"2017-10-11T14:37:05Z","abstract_excerpt":"Bayesian inference of Gibbs random fields (GRFs) is often referred to as a doubly intractable problem, since the likelihood function is intractable. The exploration of the posterior distribution of such models is typically carried out with a sophisticated Markov chain Monte Carlo (MCMC) method, the exchange algorithm (Murray et al., 2006), which requires simulations from the likelihood function at each iteration. The purpose of this paper is to consider an approach to dramatically reduce this computational overhead. To this end we introduce a novel class of algorithms which use realizations of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04093","kind":"arxiv","version":2},"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":"2026-05-18T00:32:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K3xmu29FnywxmibjQW+PgyFOwXg4ZgG9zli2uX5BM0BtP+VLoUdYXT1Hjc/AuVxIt/eNAtIdfHMt397Vct2qCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-29T22:44:42.804013Z"},"content_sha256":"0c8d8ad0c0942cb641491e0a114756420824ae3d44feffb2004b573205bc8f2c","schema_version":"1.0","event_id":"sha256:0c8d8ad0c0942cb641491e0a114756420824ae3d44feffb2004b573205bc8f2c"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3HWXZO3IUCOUVBJR5FSGHPHM6Y/bundle.json","state_url":"https://pith.science/pith/3HWXZO3IUCOUVBJR5FSGHPHM6Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3HWXZO3IUCOUVBJR5FSGHPHM6Y/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":"2026-06-29T22:44:42Z","links":{"resolver":"https://pith.science/pith/3HWXZO3IUCOUVBJR5FSGHPHM6Y","bundle":"https://pith.science/pith/3HWXZO3IUCOUVBJR5FSGHPHM6Y/bundle.json","state":"https://pith.science/pith/3HWXZO3IUCOUVBJR5FSGHPHM6Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3HWXZO3IUCOUVBJR5FSGHPHM6Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:3HWXZO3IUCOUVBJR5FSGHPHM6Y","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":"be282990ad054703986dacfe7abb0ebd95a906431b4a34e81c7fdb4636dd70c2","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-10-11T14:37:05Z","title_canon_sha256":"d624c901f17db97350f0887d8f55212861905d622d7b1db1edc081da05d8772b"},"schema_version":"1.0","source":{"id":"1710.04093","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1710.04093","created_at":"2026-05-18T00:32:57Z"},{"alias_kind":"arxiv_version","alias_value":"1710.04093v2","created_at":"2026-05-18T00:32:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1710.04093","created_at":"2026-05-18T00:32:57Z"},{"alias_kind":"pith_short_12","alias_value":"3HWXZO3IUCOU","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"3HWXZO3IUCOUVBJR","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"3HWXZO3I","created_at":"2026-05-18T12:30:58Z"}],"graph_snapshots":[{"event_id":"sha256:0c8d8ad0c0942cb641491e0a114756420824ae3d44feffb2004b573205bc8f2c","target":"graph","created_at":"2026-05-18T00:32:57Z","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":"Bayesian inference of Gibbs random fields (GRFs) is often referred to as a doubly intractable problem, since the likelihood function is intractable. The exploration of the posterior distribution of such models is typically carried out with a sophisticated Markov chain Monte Carlo (MCMC) method, the exchange algorithm (Murray et al., 2006), which requires simulations from the likelihood function at each iteration. The purpose of this paper is to consider an approach to dramatically reduce this computational overhead. To this end we introduce a novel class of algorithms which use realizations of","authors_text":"Aidan Boland, Florian Maire, Nial Friel","cross_cats":["stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-10-11T14:37:05Z","title":"Efficient MCMC for Gibbs Random Fields using pre-computation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1710.04093","kind":"arxiv","version":2},"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:bd5f69becd1c8422b961aba65739d7e23c2824729dcd0d3b6877272b0f6e3316","target":"record","created_at":"2026-05-18T00:32:57Z","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":"be282990ad054703986dacfe7abb0ebd95a906431b4a34e81c7fdb4636dd70c2","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-10-11T14:37:05Z","title_canon_sha256":"d624c901f17db97350f0887d8f55212861905d622d7b1db1edc081da05d8772b"},"schema_version":"1.0","source":{"id":"1710.04093","kind":"arxiv","version":2}},"canonical_sha256":"d9ed7cbb68a09d4a8531e96463bcecf613cce5b38002be3e3bec5aa707706a95","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d9ed7cbb68a09d4a8531e96463bcecf613cce5b38002be3e3bec5aa707706a95","first_computed_at":"2026-05-18T00:32:57.141926Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:32:57.141926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jfQLpNLoxoGSbOWsdSl4wtoIGYBo7dlskdU2XwOkfw6p+jWD2dNnjCjhV7PNYUMLHQBuJAh6rVzESlIe2F0xAA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:32:57.142611Z","signed_message":"canonical_sha256_bytes"},"source_id":"1710.04093","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:bd5f69becd1c8422b961aba65739d7e23c2824729dcd0d3b6877272b0f6e3316","sha256:0c8d8ad0c0942cb641491e0a114756420824ae3d44feffb2004b573205bc8f2c"],"state_sha256":"c75cb32a52367f92cd06dda4115265ff9a16091f543dc2ac62ef00a7e7b8d2d6"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lyKliqxDqH1/msNmiSddUc5cg89w4aNzkWFJgIWr/ULF/3wT0X5wp85xFmDCQhI2d4wStBYA1ay0ylYVz7jxBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-29T22:44:42.806214Z","bundle_sha256":"ba86d2fafef631e59402f367afdede2fa7a153e72b3610a0fbd79cfdd01447f1"}}