{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:O7QPMSQ6ZZ6VHN6VMCWWTJMDUO","short_pith_number":"pith:O7QPMSQ6","canonical_record":{"source":{"id":"2502.04990","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2025-02-07T15:10:42Z","cross_cats_sorted":[],"title_canon_sha256":"a6cf3475223b6e84abddf3e180e140752f317539f4c1121155162859fa7ef8b3","abstract_canon_sha256":"708d541b334fc28aace16fd23c17f25840d65a328017d37eab6f4f10ad79ff10"},"schema_version":"1.0"},"canonical_sha256":"77e0f64a1ece7d53b7d560ad69a583a39f3ae3a823bd5e87275fb9859b67ab23","source":{"kind":"arxiv","id":"2502.04990","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.04990","created_at":"2026-07-05T10:11:00Z"},{"alias_kind":"arxiv_version","alias_value":"2502.04990v1","created_at":"2026-07-05T10:11:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.04990","created_at":"2026-07-05T10:11:00Z"},{"alias_kind":"pith_short_12","alias_value":"O7QPMSQ6ZZ6V","created_at":"2026-07-05T10:11:00Z"},{"alias_kind":"pith_short_16","alias_value":"O7QPMSQ6ZZ6VHN6V","created_at":"2026-07-05T10:11:00Z"},{"alias_kind":"pith_short_8","alias_value":"O7QPMSQ6","created_at":"2026-07-05T10:11:00Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:O7QPMSQ6ZZ6VHN6VMCWWTJMDUO","target":"record","payload":{"canonical_record":{"source":{"id":"2502.04990","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2025-02-07T15:10:42Z","cross_cats_sorted":[],"title_canon_sha256":"a6cf3475223b6e84abddf3e180e140752f317539f4c1121155162859fa7ef8b3","abstract_canon_sha256":"708d541b334fc28aace16fd23c17f25840d65a328017d37eab6f4f10ad79ff10"},"schema_version":"1.0"},"canonical_sha256":"77e0f64a1ece7d53b7d560ad69a583a39f3ae3a823bd5e87275fb9859b67ab23","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:11:00.977023Z","signature_b64":"xv4kuJ8gqsU+oVOR9woXss/Wqzv8dY6IBugO7IZxLWnEC+6F4h/1hZLimuQ9iuav1j0qJbpc4gfDjrptbY9IBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77e0f64a1ece7d53b7d560ad69a583a39f3ae3a823bd5e87275fb9859b67ab23","last_reissued_at":"2026-07-05T10:11:00.976481Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:11:00.976481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.04990","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:11:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BvoJQNGSaXnY247EXi/wsG04mKcjC+6/ybtKdg/hU8G5Gkr90v36/MeKDA3LO/5OHQNoQVLevnYpBYdz3vNjBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T08:41:36.779429Z"},"content_sha256":"eb6a5993dcbb247be9fe272cb7d459113f0c881f2142e5f73e594ee8ee6a2679","schema_version":"1.0","event_id":"sha256:eb6a5993dcbb247be9fe272cb7d459113f0c881f2142e5f73e594ee8ee6a2679"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:O7QPMSQ6ZZ6VHN6VMCWWTJMDUO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Probabilistic Programming with Sufficient Statistics for faster Bayesian Computation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Alejandra Avalos-Pacheco, Clemens Pichler, Jack Jewson","submitted_at":"2025-02-07T15:10:42Z","abstract_excerpt":"Probabilistic programming methods have revolutionised Bayesian inference, making it easier than ever for practitioners to perform Markov-chain-Monte-Carlo sampling from non-conjugate posterior distributions. Here we focus on Stan, arguably the most used probabilistic programming tool for Bayesian inference (Carpenter et al., 2017), and its interface with R via the brms (Burkner, 2017) and rstanarm (Goodrich et al., 2024) packages. Although easy to implement, these tools can become computationally prohibitive when applied to datasets with many observations or models with numerous parameters. Wh"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.04990","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/2502.04990/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:11:00Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5bIkq6/PKGCtdsfvWXV1+WFdo+sXmhvB+e/Nr4Cand8os/+bLkO/JZpBvkO83kzzZWq9Fs1V9J+Af5LXLCJdCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-14T08:41:36.780091Z"},"content_sha256":"4fdedf16f4ea11d4e5834cf1f85898cb7a0536664d16493520c5e2773a64020d","schema_version":"1.0","event_id":"sha256:4fdedf16f4ea11d4e5834cf1f85898cb7a0536664d16493520c5e2773a64020d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/O7QPMSQ6ZZ6VHN6VMCWWTJMDUO/bundle.json","state_url":"https://pith.science/pith/O7QPMSQ6ZZ6VHN6VMCWWTJMDUO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/O7QPMSQ6ZZ6VHN6VMCWWTJMDUO/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":"2026-08-14T08:41:36Z","links":{"resolver":"https://pith.science/pith/O7QPMSQ6ZZ6VHN6VMCWWTJMDUO","bundle":"https://pith.science/pith/O7QPMSQ6ZZ6VHN6VMCWWTJMDUO/bundle.json","state":"https://pith.science/pith/O7QPMSQ6ZZ6VHN6VMCWWTJMDUO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/O7QPMSQ6ZZ6VHN6VMCWWTJMDUO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:O7QPMSQ6ZZ6VHN6VMCWWTJMDUO","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":"708d541b334fc28aace16fd23c17f25840d65a328017d37eab6f4f10ad79ff10","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2025-02-07T15:10:42Z","title_canon_sha256":"a6cf3475223b6e84abddf3e180e140752f317539f4c1121155162859fa7ef8b3"},"schema_version":"1.0","source":{"id":"2502.04990","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.04990","created_at":"2026-07-05T10:11:00Z"},{"alias_kind":"arxiv_version","alias_value":"2502.04990v1","created_at":"2026-07-05T10:11:00Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.04990","created_at":"2026-07-05T10:11:00Z"},{"alias_kind":"pith_short_12","alias_value":"O7QPMSQ6ZZ6V","created_at":"2026-07-05T10:11:00Z"},{"alias_kind":"pith_short_16","alias_value":"O7QPMSQ6ZZ6VHN6V","created_at":"2026-07-05T10:11:00Z"},{"alias_kind":"pith_short_8","alias_value":"O7QPMSQ6","created_at":"2026-07-05T10:11:00Z"}],"graph_snapshots":[{"event_id":"sha256:4fdedf16f4ea11d4e5834cf1f85898cb7a0536664d16493520c5e2773a64020d","target":"graph","created_at":"2026-07-05T10:11:00Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2502.04990/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Probabilistic programming methods have revolutionised Bayesian inference, making it easier than ever for practitioners to perform Markov-chain-Monte-Carlo sampling from non-conjugate posterior distributions. Here we focus on Stan, arguably the most used probabilistic programming tool for Bayesian inference (Carpenter et al., 2017), and its interface with R via the brms (Burkner, 2017) and rstanarm (Goodrich et al., 2024) packages. Although easy to implement, these tools can become computationally prohibitive when applied to datasets with many observations or models with numerous parameters. Wh","authors_text":"Alejandra Avalos-Pacheco, Clemens Pichler, Jack Jewson","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2025-02-07T15:10:42Z","title":"Probabilistic Programming with Sufficient Statistics for faster Bayesian Computation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.04990","kind":"arxiv","version":1},"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:eb6a5993dcbb247be9fe272cb7d459113f0c881f2142e5f73e594ee8ee6a2679","target":"record","created_at":"2026-07-05T10:11:00Z","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":"708d541b334fc28aace16fd23c17f25840d65a328017d37eab6f4f10ad79ff10","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.CO","submitted_at":"2025-02-07T15:10:42Z","title_canon_sha256":"a6cf3475223b6e84abddf3e180e140752f317539f4c1121155162859fa7ef8b3"},"schema_version":"1.0","source":{"id":"2502.04990","kind":"arxiv","version":1}},"canonical_sha256":"77e0f64a1ece7d53b7d560ad69a583a39f3ae3a823bd5e87275fb9859b67ab23","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"77e0f64a1ece7d53b7d560ad69a583a39f3ae3a823bd5e87275fb9859b67ab23","first_computed_at":"2026-07-05T10:11:00.976481Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:11:00.976481Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xv4kuJ8gqsU+oVOR9woXss/Wqzv8dY6IBugO7IZxLWnEC+6F4h/1hZLimuQ9iuav1j0qJbpc4gfDjrptbY9IBA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:11:00.977023Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.04990","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eb6a5993dcbb247be9fe272cb7d459113f0c881f2142e5f73e594ee8ee6a2679","sha256:4fdedf16f4ea11d4e5834cf1f85898cb7a0536664d16493520c5e2773a64020d"],"state_sha256":"bfbc522cb970afea973212e68a696809fc2e9a922e401912a9ebe0553fce69ec"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8/u0nmlkNg6/wfp8IqwFFOKt5rJAPsJaWM+SVZS/FsYsvegn85kag2feAwbJL6PoWaCw7U4bJ4t8HqSF4wvZAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-14T08:41:36.784658Z","bundle_sha256":"640c83afb4622baefb02c2a2fb410e249cfad7a29c86097f123890c886232573"}}