{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:KV6SOJCC66VZAP545SYLKTGP26","short_pith_number":"pith:KV6SOJCC","canonical_record":{"source":{"id":"1706.04780","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-06-15T09:09:11Z","cross_cats_sorted":[],"title_canon_sha256":"d8d963ad5f7f63e2875f784283109a0be4b2e1a99a6aabd4090de95b72a5ef28","abstract_canon_sha256":"4fe4fd928349b8390472673c75d241307d64124a4786375a39983cd7cc4baddd"},"schema_version":"1.0"},"canonical_sha256":"557d272442f7ab903fbcecb0b54ccfd7b585389a26ad3590777d303d3e4c578b","source":{"kind":"arxiv","id":"1706.04780","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.04780","created_at":"2026-05-18T00:42:11Z"},{"alias_kind":"arxiv_version","alias_value":"1706.04780v2","created_at":"2026-05-18T00:42:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.04780","created_at":"2026-05-18T00:42:11Z"},{"alias_kind":"pith_short_12","alias_value":"KV6SOJCC66VZ","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"KV6SOJCC66VZAP54","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"KV6SOJCC","created_at":"2026-05-18T12:31:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:KV6SOJCC66VZAP545SYLKTGP26","target":"record","payload":{"canonical_record":{"source":{"id":"1706.04780","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-06-15T09:09:11Z","cross_cats_sorted":[],"title_canon_sha256":"d8d963ad5f7f63e2875f784283109a0be4b2e1a99a6aabd4090de95b72a5ef28","abstract_canon_sha256":"4fe4fd928349b8390472673c75d241307d64124a4786375a39983cd7cc4baddd"},"schema_version":"1.0"},"canonical_sha256":"557d272442f7ab903fbcecb0b54ccfd7b585389a26ad3590777d303d3e4c578b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:11.844302Z","signature_b64":"zPS3VA9EDXHL88WXhIvHl+ILmNU2kEIa2ZYr85Joto69MjoDq9iBAytaMRdkHfZbdD09j17IRzUPAcoQ5vNODw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"557d272442f7ab903fbcecb0b54ccfd7b585389a26ad3590777d303d3e4c578b","last_reissued_at":"2026-05-18T00:42:11.843721Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:11.843721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.04780","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":"2026-05-18T00:42:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Xvw4QsQ0IOOvp8DM35vacoqJ6JLplnD1q+dxqRBI91r1N1DHzFIEktl5ImD/pLE2akJ8L2kCyNyxhPu39Av4DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T09:42:23.831422Z"},"content_sha256":"d6b73fc5ea564b6a26fc59f7de90a1e0b5680f0541a0f45ed07415b52c32039b","schema_version":"1.0","event_id":"sha256:d6b73fc5ea564b6a26fc59f7de90a1e0b5680f0541a0f45ed07415b52c32039b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:KV6SOJCC66VZAP545SYLKTGP26","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Average of Recentered Parallel MCMC for Big Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Changye Wu, Christian P. Robert","submitted_at":"2017-06-15T09:09:11Z","abstract_excerpt":"In big data context, traditional MCMC methods, such as Metropolis-Hastings algorithms and hybrid Monte Carlo, scale poorly because of their need to evaluate the likelihood over the whole data set at each iteration. In order to resurrect MCMC methods, numerous approaches belonging to two categories: divide-and-conquer and subsampling, are proposed. In this article, we study the parallel MCMC and propose a new combination method in the divide-and-conquer framework. Compared with some parallel MCMC methods, such as consensus Monte Carlo, Weierstrass Sampler, instead of sampling from subposteriors"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.04780","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":"2026-05-18T00:42:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8PBzVFV7gXoxqSek9FjlIz+xCYOAfGWgA2SRb21ncYggkjY4JrOfsh0y7yrZzkVjCPku2jErONDOMxfLZUmfBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T09:42:23.831772Z"},"content_sha256":"75cfe23f7052af0634057185b767c4cb3159dc962e89cba8e21b691ac4b01faf","schema_version":"1.0","event_id":"sha256:75cfe23f7052af0634057185b767c4cb3159dc962e89cba8e21b691ac4b01faf"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/KV6SOJCC66VZAP545SYLKTGP26/bundle.json","state_url":"https://pith.science/pith/KV6SOJCC66VZAP545SYLKTGP26/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/KV6SOJCC66VZAP545SYLKTGP26/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":"2026-06-02T09:42:23Z","links":{"resolver":"https://pith.science/pith/KV6SOJCC66VZAP545SYLKTGP26","bundle":"https://pith.science/pith/KV6SOJCC66VZAP545SYLKTGP26/bundle.json","state":"https://pith.science/pith/KV6SOJCC66VZAP545SYLKTGP26/state.json","well_known_bundle":"https://pith.science/.well-known/pith/KV6SOJCC66VZAP545SYLKTGP26/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:KV6SOJCC66VZAP545SYLKTGP26","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":"4fe4fd928349b8390472673c75d241307d64124a4786375a39983cd7cc4baddd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-06-15T09:09:11Z","title_canon_sha256":"d8d963ad5f7f63e2875f784283109a0be4b2e1a99a6aabd4090de95b72a5ef28"},"schema_version":"1.0","source":{"id":"1706.04780","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.04780","created_at":"2026-05-18T00:42:11Z"},{"alias_kind":"arxiv_version","alias_value":"1706.04780v2","created_at":"2026-05-18T00:42:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.04780","created_at":"2026-05-18T00:42:11Z"},{"alias_kind":"pith_short_12","alias_value":"KV6SOJCC66VZ","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_16","alias_value":"KV6SOJCC66VZAP54","created_at":"2026-05-18T12:31:28Z"},{"alias_kind":"pith_short_8","alias_value":"KV6SOJCC","created_at":"2026-05-18T12:31:28Z"}],"graph_snapshots":[{"event_id":"sha256:75cfe23f7052af0634057185b767c4cb3159dc962e89cba8e21b691ac4b01faf","target":"graph","created_at":"2026-05-18T00:42:11Z","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":"In big data context, traditional MCMC methods, such as Metropolis-Hastings algorithms and hybrid Monte Carlo, scale poorly because of their need to evaluate the likelihood over the whole data set at each iteration. In order to resurrect MCMC methods, numerous approaches belonging to two categories: divide-and-conquer and subsampling, are proposed. In this article, we study the parallel MCMC and propose a new combination method in the divide-and-conquer framework. Compared with some parallel MCMC methods, such as consensus Monte Carlo, Weierstrass Sampler, instead of sampling from subposteriors","authors_text":"Changye Wu, Christian P. Robert","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-06-15T09:09:11Z","title":"Average of Recentered Parallel MCMC for Big Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.04780","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:d6b73fc5ea564b6a26fc59f7de90a1e0b5680f0541a0f45ed07415b52c32039b","target":"record","created_at":"2026-05-18T00:42:11Z","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":"4fe4fd928349b8390472673c75d241307d64124a4786375a39983cd7cc4baddd","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-06-15T09:09:11Z","title_canon_sha256":"d8d963ad5f7f63e2875f784283109a0be4b2e1a99a6aabd4090de95b72a5ef28"},"schema_version":"1.0","source":{"id":"1706.04780","kind":"arxiv","version":2}},"canonical_sha256":"557d272442f7ab903fbcecb0b54ccfd7b585389a26ad3590777d303d3e4c578b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"557d272442f7ab903fbcecb0b54ccfd7b585389a26ad3590777d303d3e4c578b","first_computed_at":"2026-05-18T00:42:11.843721Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:42:11.843721Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zPS3VA9EDXHL88WXhIvHl+ILmNU2kEIa2ZYr85Joto69MjoDq9iBAytaMRdkHfZbdD09j17IRzUPAcoQ5vNODw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:42:11.844302Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.04780","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d6b73fc5ea564b6a26fc59f7de90a1e0b5680f0541a0f45ed07415b52c32039b","sha256:75cfe23f7052af0634057185b767c4cb3159dc962e89cba8e21b691ac4b01faf"],"state_sha256":"f62681d65f840c8a4adb6c61c62cfadd31e973d9e8a0112e1cedd36f34816b13"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+uY9ysGzAoBypfx9gKPZUaLcOawEs5IDGuIPB+2dFTyQ/BmnMiDFVWCa+2Bip+UhgiRNIUTMIvJ8ZgfnG647AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T09:42:23.833752Z","bundle_sha256":"8d14d8c64bc4d5006ee918e1a853bc98172946606657aecb25b2d52dfc0e58f2"}}