{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:VX2CAAU3QJJKVHAZN2JKWB6XT3","short_pith_number":"pith:VX2CAAU3","canonical_record":{"source":{"id":"1201.4529","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-01-22T06:05:29Z","cross_cats_sorted":[],"title_canon_sha256":"7cae7cb25a09b68eef255645572edab6b84e0a30896329fa2c3aeecc123c155a","abstract_canon_sha256":"00287acfdfb19ec30d660195729d9d3ff405df2c4d7b7d3d26c640ab99e8bbee"},"schema_version":"1.0"},"canonical_sha256":"adf420029b8252aa9c196e92ab07d79ec79e13ba185af3fb312d3ee896fc612d","source":{"kind":"arxiv","id":"1201.4529","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1201.4529","created_at":"2026-05-18T04:04:05Z"},{"alias_kind":"arxiv_version","alias_value":"1201.4529v1","created_at":"2026-05-18T04:04:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1201.4529","created_at":"2026-05-18T04:04:05Z"},{"alias_kind":"pith_short_12","alias_value":"VX2CAAU3QJJK","created_at":"2026-05-18T12:27:25Z"},{"alias_kind":"pith_short_16","alias_value":"VX2CAAU3QJJKVHAZ","created_at":"2026-05-18T12:27:25Z"},{"alias_kind":"pith_short_8","alias_value":"VX2CAAU3","created_at":"2026-05-18T12:27:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:VX2CAAU3QJJKVHAZN2JKWB6XT3","target":"record","payload":{"canonical_record":{"source":{"id":"1201.4529","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-01-22T06:05:29Z","cross_cats_sorted":[],"title_canon_sha256":"7cae7cb25a09b68eef255645572edab6b84e0a30896329fa2c3aeecc123c155a","abstract_canon_sha256":"00287acfdfb19ec30d660195729d9d3ff405df2c4d7b7d3d26c640ab99e8bbee"},"schema_version":"1.0"},"canonical_sha256":"adf420029b8252aa9c196e92ab07d79ec79e13ba185af3fb312d3ee896fc612d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:04:05.812248Z","signature_b64":"7CzW9QRBZjXkjJcwHsGcZ71aomUnVUoX+VtI4hSW1SydFAXX3BP0ldhUz5Htj6knM0Eq4sJpCnTt8s9crJGLBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"adf420029b8252aa9c196e92ab07d79ec79e13ba185af3fb312d3ee896fc612d","last_reissued_at":"2026-05-18T04:04:05.811631Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:04:05.811631Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1201.4529","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":"2026-05-18T04:04:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"t4DkxRbz/coMaB7xdVdl4z+sKvGZhZffOpMqEdSCkhOz0CdYGpAUrIjdBj4IDjrpIN51qj84eYU2gdBFJj/xAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T16:32:02.552908Z"},"content_sha256":"67a129b96c67e7ff2e2c1eaca2a0a7e71e93ee8307558573e15dd64263c1186f","schema_version":"1.0","event_id":"sha256:67a129b96c67e7ff2e2c1eaca2a0a7e71e93ee8307558573e15dd64263c1186f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:VX2CAAU3QJJKVHAZN2JKWB6XT3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Inference for a Class of Partially Observed Point Process Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Ajay Jasra, Emma McCoy, James S. Martin","submitted_at":"2012-01-22T06:05:29Z","abstract_excerpt":"This paper presents a simulation-based framework for sequential inference from partially and discretely observed point process (PP's) models with static parameters. Taking on a Bayesian perspective for the static parameters, we build upon sequential Monte Carlo (SMC) methods, investigating the problems of performing sequential filtering and smoothing in complex examples, where current methods often fail. We consider various approaches for approximating posterior distributions using SMC. Our approaches, with some theoretical discussion are illustrated on a doubly stochastic point process applie"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1201.4529","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":""},"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":"2026-05-18T04:04:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CfzO8ZmS9RUPFZTbP8WfoC0ul4n5NOQiZv+KlwFbOE3GyqlG6ZDXzV07GrPkNOybMte8Z8GmApyT6yUqtwQZCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T16:32:02.553557Z"},"content_sha256":"c731ce703b0b91f1c695e3b35408e1b12f173370d9c255f664db78cae447144a","schema_version":"1.0","event_id":"sha256:c731ce703b0b91f1c695e3b35408e1b12f173370d9c255f664db78cae447144a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VX2CAAU3QJJKVHAZN2JKWB6XT3/bundle.json","state_url":"https://pith.science/pith/VX2CAAU3QJJKVHAZN2JKWB6XT3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VX2CAAU3QJJKVHAZN2JKWB6XT3/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":"2026-05-28T16:32:02Z","links":{"resolver":"https://pith.science/pith/VX2CAAU3QJJKVHAZN2JKWB6XT3","bundle":"https://pith.science/pith/VX2CAAU3QJJKVHAZN2JKWB6XT3/bundle.json","state":"https://pith.science/pith/VX2CAAU3QJJKVHAZN2JKWB6XT3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VX2CAAU3QJJKVHAZN2JKWB6XT3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:VX2CAAU3QJJKVHAZN2JKWB6XT3","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":"00287acfdfb19ec30d660195729d9d3ff405df2c4d7b7d3d26c640ab99e8bbee","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-01-22T06:05:29Z","title_canon_sha256":"7cae7cb25a09b68eef255645572edab6b84e0a30896329fa2c3aeecc123c155a"},"schema_version":"1.0","source":{"id":"1201.4529","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1201.4529","created_at":"2026-05-18T04:04:05Z"},{"alias_kind":"arxiv_version","alias_value":"1201.4529v1","created_at":"2026-05-18T04:04:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1201.4529","created_at":"2026-05-18T04:04:05Z"},{"alias_kind":"pith_short_12","alias_value":"VX2CAAU3QJJK","created_at":"2026-05-18T12:27:25Z"},{"alias_kind":"pith_short_16","alias_value":"VX2CAAU3QJJKVHAZ","created_at":"2026-05-18T12:27:25Z"},{"alias_kind":"pith_short_8","alias_value":"VX2CAAU3","created_at":"2026-05-18T12:27:25Z"}],"graph_snapshots":[{"event_id":"sha256:c731ce703b0b91f1c695e3b35408e1b12f173370d9c255f664db78cae447144a","target":"graph","created_at":"2026-05-18T04:04:05Z","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":"This paper presents a simulation-based framework for sequential inference from partially and discretely observed point process (PP's) models with static parameters. Taking on a Bayesian perspective for the static parameters, we build upon sequential Monte Carlo (SMC) methods, investigating the problems of performing sequential filtering and smoothing in complex examples, where current methods often fail. We consider various approaches for approximating posterior distributions using SMC. Our approaches, with some theoretical discussion are illustrated on a doubly stochastic point process applie","authors_text":"Ajay Jasra, Emma McCoy, James S. Martin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-01-22T06:05:29Z","title":"Inference for a Class of Partially Observed Point Process Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1201.4529","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":"sha256:67a129b96c67e7ff2e2c1eaca2a0a7e71e93ee8307558573e15dd64263c1186f","target":"record","created_at":"2026-05-18T04:04:05Z","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":"00287acfdfb19ec30d660195729d9d3ff405df2c4d7b7d3d26c640ab99e8bbee","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-01-22T06:05:29Z","title_canon_sha256":"7cae7cb25a09b68eef255645572edab6b84e0a30896329fa2c3aeecc123c155a"},"schema_version":"1.0","source":{"id":"1201.4529","kind":"arxiv","version":1}},"canonical_sha256":"adf420029b8252aa9c196e92ab07d79ec79e13ba185af3fb312d3ee896fc612d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"adf420029b8252aa9c196e92ab07d79ec79e13ba185af3fb312d3ee896fc612d","first_computed_at":"2026-05-18T04:04:05.811631Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T04:04:05.811631Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7CzW9QRBZjXkjJcwHsGcZ71aomUnVUoX+VtI4hSW1SydFAXX3BP0ldhUz5Htj6knM0Eq4sJpCnTt8s9crJGLBA==","signature_status":"signed_v1","signed_at":"2026-05-18T04:04:05.812248Z","signed_message":"canonical_sha256_bytes"},"source_id":"1201.4529","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:67a129b96c67e7ff2e2c1eaca2a0a7e71e93ee8307558573e15dd64263c1186f","sha256:c731ce703b0b91f1c695e3b35408e1b12f173370d9c255f664db78cae447144a"],"state_sha256":"bfe1617f0196679d4f5b933db153ac996c88e05914027a5fee5e0beb25f7b18a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kSHilDLZxUSSBoc649KaVTaDItr9ezU1BnUUEDQBlz3F2zHulwrOw8vtwipKSxeklzJqhbiA4OeZR/sjqfVAAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T16:32:02.556910Z","bundle_sha256":"9417a9ca73a6d0544fc9665e31e1c3e884ea2e687b4b4d80ded466e47f1caad7"}}