{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:Y4FKIJK45ZUM76Z27ONDIXGXE7","short_pith_number":"pith:Y4FKIJK4","canonical_record":{"source":{"id":"1404.4178","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-04-16T09:33:36Z","cross_cats_sorted":["stat.CO","stat.ML"],"title_canon_sha256":"e6ab8d8af611b8b8e6edd59662636e0e8b31f936f0bf3f23fa7216222c793e71","abstract_canon_sha256":"832faafe05a79c9d0282104ac58b0f5681f619320dd534d0701d39051b20a8eb"},"schema_version":"1.0"},"canonical_sha256":"c70aa4255cee68cffb3afb9a345cd727c8bd71210cd3edfb2134856d86cae992","source":{"kind":"arxiv","id":"1404.4178","version":6},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1404.4178","created_at":"2026-05-17T23:57:21Z"},{"alias_kind":"arxiv_version","alias_value":"1404.4178v6","created_at":"2026-05-17T23:57:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1404.4178","created_at":"2026-05-17T23:57:21Z"},{"alias_kind":"pith_short_12","alias_value":"Y4FKIJK45ZUM","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_16","alias_value":"Y4FKIJK45ZUM76Z2","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_8","alias_value":"Y4FKIJK4","created_at":"2026-05-18T12:28:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:Y4FKIJK45ZUM76Z27ONDIXGXE7","target":"record","payload":{"canonical_record":{"source":{"id":"1404.4178","kind":"arxiv","version":6},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-04-16T09:33:36Z","cross_cats_sorted":["stat.CO","stat.ML"],"title_canon_sha256":"e6ab8d8af611b8b8e6edd59662636e0e8b31f936f0bf3f23fa7216222c793e71","abstract_canon_sha256":"832faafe05a79c9d0282104ac58b0f5681f619320dd534d0701d39051b20a8eb"},"schema_version":"1.0"},"canonical_sha256":"c70aa4255cee68cffb3afb9a345cd727c8bd71210cd3edfb2134856d86cae992","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:57:21.964414Z","signature_b64":"qvuXDfl5WXEht/gN13ajC6R6UcYbmOtByN6p9ooseXptGDiJ3wVqKwF9aOmLWNjnstz2UXZMQbwc3xLv9WKFAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c70aa4255cee68cffb3afb9a345cd727c8bd71210cd3edfb2134856d86cae992","last_reissued_at":"2026-05-17T23:57:21.963801Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:57:21.963801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1404.4178","source_version":6,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:57:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZKyzkdBNUvCjM8B3U0vPu0niVRNWXe6mKO099LQ1Jj+ZMCNgkfX4jKiZKYisWuxs+rtM9EmoJb3fwRYpQ1PXDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T14:41:07.947974Z"},"content_sha256":"ddd25f3810d3e0dabb8084751aed02bee44a20f6ca7a85f8e83e520ff77bf0fe","schema_version":"1.0","event_id":"sha256:ddd25f3810d3e0dabb8084751aed02bee44a20f6ca7a85f8e83e520ff77bf0fe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:Y4FKIJK45ZUM76Z27ONDIXGXE7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Speeding Up MCMC by Efficient Data Subsampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.CO","stat.ML"],"primary_cat":"stat.ME","authors_text":"Matias Quiroz, Mattias Villani, Minh-Ngoc Tran, Robert Kohn","submitted_at":"2014-04-16T09:33:36Z","abstract_excerpt":"We propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework where the likelihood function for $n$ observations is estimated from a random subset of $m$ observations. We introduce a highly efficient unbiased estimator of the log-likelihood based on control variates, such that the computing cost is much smaller than that of the full log-likelihood in standard MCMC. The likelihood estimate is bias-corrected and used in two dependent pseudo-marginal algorithms to sample from a perturbed posterior, for which we derive the asymptotic error with respect to $n$ and $m$, respectively. We p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1404.4178","kind":"arxiv","version":6},"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":"2026-05-17T23:57:21Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1sR3Ek/xn8iOrZ0PtJ0cQj5kCtRyuBShqm3yCxHkRF0R3UgVRk4da/FqkqrucysdWFOH+Num8ibq2RZBTW4IDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T14:41:07.948650Z"},"content_sha256":"ddd8e75204c0fc7a1c261839bc6aa12b0fe41341d806cc88419909685ac816ae","schema_version":"1.0","event_id":"sha256:ddd8e75204c0fc7a1c261839bc6aa12b0fe41341d806cc88419909685ac816ae"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Y4FKIJK45ZUM76Z27ONDIXGXE7/bundle.json","state_url":"https://pith.science/pith/Y4FKIJK45ZUM76Z27ONDIXGXE7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Y4FKIJK45ZUM76Z27ONDIXGXE7/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":"2026-05-27T14:41:07Z","links":{"resolver":"https://pith.science/pith/Y4FKIJK45ZUM76Z27ONDIXGXE7","bundle":"https://pith.science/pith/Y4FKIJK45ZUM76Z27ONDIXGXE7/bundle.json","state":"https://pith.science/pith/Y4FKIJK45ZUM76Z27ONDIXGXE7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Y4FKIJK45ZUM76Z27ONDIXGXE7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:Y4FKIJK45ZUM76Z27ONDIXGXE7","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":"832faafe05a79c9d0282104ac58b0f5681f619320dd534d0701d39051b20a8eb","cross_cats_sorted":["stat.CO","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-04-16T09:33:36Z","title_canon_sha256":"e6ab8d8af611b8b8e6edd59662636e0e8b31f936f0bf3f23fa7216222c793e71"},"schema_version":"1.0","source":{"id":"1404.4178","kind":"arxiv","version":6}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1404.4178","created_at":"2026-05-17T23:57:21Z"},{"alias_kind":"arxiv_version","alias_value":"1404.4178v6","created_at":"2026-05-17T23:57:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1404.4178","created_at":"2026-05-17T23:57:21Z"},{"alias_kind":"pith_short_12","alias_value":"Y4FKIJK45ZUM","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_16","alias_value":"Y4FKIJK45ZUM76Z2","created_at":"2026-05-18T12:28:57Z"},{"alias_kind":"pith_short_8","alias_value":"Y4FKIJK4","created_at":"2026-05-18T12:28:57Z"}],"graph_snapshots":[{"event_id":"sha256:ddd8e75204c0fc7a1c261839bc6aa12b0fe41341d806cc88419909685ac816ae","target":"graph","created_at":"2026-05-17T23:57:21Z","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 propose Subsampling MCMC, a Markov Chain Monte Carlo (MCMC) framework where the likelihood function for $n$ observations is estimated from a random subset of $m$ observations. We introduce a highly efficient unbiased estimator of the log-likelihood based on control variates, such that the computing cost is much smaller than that of the full log-likelihood in standard MCMC. The likelihood estimate is bias-corrected and used in two dependent pseudo-marginal algorithms to sample from a perturbed posterior, for which we derive the asymptotic error with respect to $n$ and $m$, respectively. We p","authors_text":"Matias Quiroz, Mattias Villani, Minh-Ngoc Tran, Robert Kohn","cross_cats":["stat.CO","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-04-16T09:33:36Z","title":"Speeding Up MCMC by Efficient Data Subsampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1404.4178","kind":"arxiv","version":6},"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:ddd25f3810d3e0dabb8084751aed02bee44a20f6ca7a85f8e83e520ff77bf0fe","target":"record","created_at":"2026-05-17T23:57:21Z","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":"832faafe05a79c9d0282104ac58b0f5681f619320dd534d0701d39051b20a8eb","cross_cats_sorted":["stat.CO","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-04-16T09:33:36Z","title_canon_sha256":"e6ab8d8af611b8b8e6edd59662636e0e8b31f936f0bf3f23fa7216222c793e71"},"schema_version":"1.0","source":{"id":"1404.4178","kind":"arxiv","version":6}},"canonical_sha256":"c70aa4255cee68cffb3afb9a345cd727c8bd71210cd3edfb2134856d86cae992","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c70aa4255cee68cffb3afb9a345cd727c8bd71210cd3edfb2134856d86cae992","first_computed_at":"2026-05-17T23:57:21.963801Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:57:21.963801Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"qvuXDfl5WXEht/gN13ajC6R6UcYbmOtByN6p9ooseXptGDiJ3wVqKwF9aOmLWNjnstz2UXZMQbwc3xLv9WKFAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:57:21.964414Z","signed_message":"canonical_sha256_bytes"},"source_id":"1404.4178","source_kind":"arxiv","source_version":6}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ddd25f3810d3e0dabb8084751aed02bee44a20f6ca7a85f8e83e520ff77bf0fe","sha256:ddd8e75204c0fc7a1c261839bc6aa12b0fe41341d806cc88419909685ac816ae"],"state_sha256":"79cea85ee2f5829dd5793b17fcc6abccdd9ab680d2cbb878735e71d7081fa6e9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"z6aatau26HNYjLS8ok/xxS1lkNVWHogrssxsnmQHzI5Q9o+X6ycDqhJlkft5rGu429fvEJKgZ/1MdYgcnOZ6BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T14:41:07.952159Z","bundle_sha256":"d5d5dce2e9dc33fb284fc22afcae76ae36f0455ffe45c659e1d63fba2d9fce25"}}