{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:EXXHKPCAVPLLXJUA3ILGO7KULW","short_pith_number":"pith:EXXHKPCA","canonical_record":{"source":{"id":"1411.3921","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-11-14T14:40:54Z","cross_cats_sorted":["astro-ph.IM","physics.data-an"],"title_canon_sha256":"14197f5915e65022afe3a4ce62f1c36aa50cc588de6e0015d7d11eeb16e763bd","abstract_canon_sha256":"cb2983605ad62bfd998df7f32a8b5a531ac586f99c43fbbeed5faecd72d7d9c4"},"schema_version":"1.0"},"canonical_sha256":"25ee753c40abd6bba680da16677d545da7e93cee0e76196b7aced6a891b5e336","source":{"kind":"arxiv","id":"1411.3921","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1411.3921","created_at":"2026-05-18T02:29:24Z"},{"alias_kind":"arxiv_version","alias_value":"1411.3921v3","created_at":"2026-05-18T02:29:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1411.3921","created_at":"2026-05-18T02:29:24Z"},{"alias_kind":"pith_short_12","alias_value":"EXXHKPCAVPLL","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_16","alias_value":"EXXHKPCAVPLLXJUA","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_8","alias_value":"EXXHKPCA","created_at":"2026-05-18T12:28:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:EXXHKPCAVPLLXJUA3ILGO7KULW","target":"record","payload":{"canonical_record":{"source":{"id":"1411.3921","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-11-14T14:40:54Z","cross_cats_sorted":["astro-ph.IM","physics.data-an"],"title_canon_sha256":"14197f5915e65022afe3a4ce62f1c36aa50cc588de6e0015d7d11eeb16e763bd","abstract_canon_sha256":"cb2983605ad62bfd998df7f32a8b5a531ac586f99c43fbbeed5faecd72d7d9c4"},"schema_version":"1.0"},"canonical_sha256":"25ee753c40abd6bba680da16677d545da7e93cee0e76196b7aced6a891b5e336","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:29:24.682542Z","signature_b64":"wuG8u0qZDJpN5MTO1scOaiiXf4tt+8pRWsP3Hwq0HKORA0Zw4S6xeGO+c4EG8pse0rgxlchFCVCpZ5ChYmctCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"25ee753c40abd6bba680da16677d545da7e93cee0e76196b7aced6a891b5e336","last_reissued_at":"2026-05-18T02:29:24.682168Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:29:24.682168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1411.3921","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:29:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/FHQ1XMYn3UknERZ056gHG2WimK3NYcsbpJKQkSYl0xcKnI+mrAmn/iAPUkCkrQflqegyogtwk1pNbIKlEblAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T07:15:24.295403Z"},"content_sha256":"b9f2b32531baaa12bb1792a08a7cc3fa833f39b81fad146d83722d0889877ea0","schema_version":"1.0","event_id":"sha256:b9f2b32531baaa12bb1792a08a7cc3fa833f39b81fad146d83722d0889877ea0"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:EXXHKPCAVPLLXJUA3ILGO7KULW","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Inference for Trans-dimensional Bayesian Models with Diffusive Nested Sampling","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["astro-ph.IM","physics.data-an"],"primary_cat":"stat.CO","authors_text":"Brendon J. Brewer","submitted_at":"2014-11-14T14:40:54Z","abstract_excerpt":"Many inference problems involve inferring the number $N$ of components in some region, along with their properties $\\{\\mathbf{x}_i\\}_{i=1}^N$, from a dataset $\\mathcal{D}$. A common statistical example is finite mixture modelling. In the Bayesian framework, these problems are typically solved using one of the following two methods: i) by executing a Monte Carlo algorithm (such as Nested Sampling) once for each possible value of $N$, and calculating the marginal likelihood or evidence as a function of $N$; or ii) by doing a single run that allows the model dimension $N$ to change (such as Marko"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1411.3921","kind":"arxiv","version":3},"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":"2026-05-18T02:29:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Bms1dAJu0JyStA/Vk5F1E1CK2q0SpNjmm4LYsuhyGoEMNuL5QCysc+ZG38+BQLafpyo/IZQ4EHsYP3kamdjkAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T07:15:24.296058Z"},"content_sha256":"7a5b4454c181c794e01a8496d03cbfe9b451e8aa220040106a5268a44362395b","schema_version":"1.0","event_id":"sha256:7a5b4454c181c794e01a8496d03cbfe9b451e8aa220040106a5268a44362395b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EXXHKPCAVPLLXJUA3ILGO7KULW/bundle.json","state_url":"https://pith.science/pith/EXXHKPCAVPLLXJUA3ILGO7KULW/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EXXHKPCAVPLLXJUA3ILGO7KULW/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":"2026-06-11T07:15:24Z","links":{"resolver":"https://pith.science/pith/EXXHKPCAVPLLXJUA3ILGO7KULW","bundle":"https://pith.science/pith/EXXHKPCAVPLLXJUA3ILGO7KULW/bundle.json","state":"https://pith.science/pith/EXXHKPCAVPLLXJUA3ILGO7KULW/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EXXHKPCAVPLLXJUA3ILGO7KULW/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:EXXHKPCAVPLLXJUA3ILGO7KULW","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":"cb2983605ad62bfd998df7f32a8b5a531ac586f99c43fbbeed5faecd72d7d9c4","cross_cats_sorted":["astro-ph.IM","physics.data-an"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-11-14T14:40:54Z","title_canon_sha256":"14197f5915e65022afe3a4ce62f1c36aa50cc588de6e0015d7d11eeb16e763bd"},"schema_version":"1.0","source":{"id":"1411.3921","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1411.3921","created_at":"2026-05-18T02:29:24Z"},{"alias_kind":"arxiv_version","alias_value":"1411.3921v3","created_at":"2026-05-18T02:29:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1411.3921","created_at":"2026-05-18T02:29:24Z"},{"alias_kind":"pith_short_12","alias_value":"EXXHKPCAVPLL","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_16","alias_value":"EXXHKPCAVPLLXJUA","created_at":"2026-05-18T12:28:28Z"},{"alias_kind":"pith_short_8","alias_value":"EXXHKPCA","created_at":"2026-05-18T12:28:28Z"}],"graph_snapshots":[{"event_id":"sha256:7a5b4454c181c794e01a8496d03cbfe9b451e8aa220040106a5268a44362395b","target":"graph","created_at":"2026-05-18T02:29:24Z","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":"Many inference problems involve inferring the number $N$ of components in some region, along with their properties $\\{\\mathbf{x}_i\\}_{i=1}^N$, from a dataset $\\mathcal{D}$. A common statistical example is finite mixture modelling. In the Bayesian framework, these problems are typically solved using one of the following two methods: i) by executing a Monte Carlo algorithm (such as Nested Sampling) once for each possible value of $N$, and calculating the marginal likelihood or evidence as a function of $N$; or ii) by doing a single run that allows the model dimension $N$ to change (such as Marko","authors_text":"Brendon J. Brewer","cross_cats":["astro-ph.IM","physics.data-an"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-11-14T14:40:54Z","title":"Inference for Trans-dimensional Bayesian Models with Diffusive Nested Sampling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1411.3921","kind":"arxiv","version":3},"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:b9f2b32531baaa12bb1792a08a7cc3fa833f39b81fad146d83722d0889877ea0","target":"record","created_at":"2026-05-18T02:29:24Z","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":"cb2983605ad62bfd998df7f32a8b5a531ac586f99c43fbbeed5faecd72d7d9c4","cross_cats_sorted":["astro-ph.IM","physics.data-an"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-11-14T14:40:54Z","title_canon_sha256":"14197f5915e65022afe3a4ce62f1c36aa50cc588de6e0015d7d11eeb16e763bd"},"schema_version":"1.0","source":{"id":"1411.3921","kind":"arxiv","version":3}},"canonical_sha256":"25ee753c40abd6bba680da16677d545da7e93cee0e76196b7aced6a891b5e336","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"25ee753c40abd6bba680da16677d545da7e93cee0e76196b7aced6a891b5e336","first_computed_at":"2026-05-18T02:29:24.682168Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:29:24.682168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wuG8u0qZDJpN5MTO1scOaiiXf4tt+8pRWsP3Hwq0HKORA0Zw4S6xeGO+c4EG8pse0rgxlchFCVCpZ5ChYmctCA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:29:24.682542Z","signed_message":"canonical_sha256_bytes"},"source_id":"1411.3921","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b9f2b32531baaa12bb1792a08a7cc3fa833f39b81fad146d83722d0889877ea0","sha256:7a5b4454c181c794e01a8496d03cbfe9b451e8aa220040106a5268a44362395b"],"state_sha256":"6b819def285791c2bc200119d77c025ef1edd68ca52f90d862017a2efa4ea504"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"WKuSJyrvFtWP3ShP4zT1GbDYPKawn7Ievd6smZs8TlxE+eVNXtfQrJhrDMrRI6ZU9MXrNFCrr7QLyuQHyOFOBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T07:15:24.300179Z","bundle_sha256":"b41af586e8bf246306f2252e5f4eded77b7038d6818d89aced2de348ece5f8e6"}}