{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:C42M535OO3DBUNJB76RZ6OT6KU","short_pith_number":"pith:C42M535O","canonical_record":{"source":{"id":"1503.02644","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-03-09T19:43:07Z","cross_cats_sorted":[],"title_canon_sha256":"d236231bc2c212c9f97fea924a924044a6ec0b388adc017732c643f3201e665b","abstract_canon_sha256":"c9838c462b7fdca9331dcc83e85d40c1aaa5737982750cd769b7728291df1ba8"},"schema_version":"1.0"},"canonical_sha256":"1734ceefae76c61a3521ffa39f3a7e553fc8f94a37d0952115e652e49cad7728","source":{"kind":"arxiv","id":"1503.02644","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1503.02644","created_at":"2026-05-18T02:25:19Z"},{"alias_kind":"arxiv_version","alias_value":"1503.02644v1","created_at":"2026-05-18T02:25:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.02644","created_at":"2026-05-18T02:25:19Z"},{"alias_kind":"pith_short_12","alias_value":"C42M535OO3DB","created_at":"2026-05-18T12:29:14Z"},{"alias_kind":"pith_short_16","alias_value":"C42M535OO3DBUNJB","created_at":"2026-05-18T12:29:14Z"},{"alias_kind":"pith_short_8","alias_value":"C42M535O","created_at":"2026-05-18T12:29:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:C42M535OO3DBUNJB76RZ6OT6KU","target":"record","payload":{"canonical_record":{"source":{"id":"1503.02644","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-03-09T19:43:07Z","cross_cats_sorted":[],"title_canon_sha256":"d236231bc2c212c9f97fea924a924044a6ec0b388adc017732c643f3201e665b","abstract_canon_sha256":"c9838c462b7fdca9331dcc83e85d40c1aaa5737982750cd769b7728291df1ba8"},"schema_version":"1.0"},"canonical_sha256":"1734ceefae76c61a3521ffa39f3a7e553fc8f94a37d0952115e652e49cad7728","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:25:19.287169Z","signature_b64":"dYB7xgDNi9f6RJK67wJVbN0tcvanpk5YqMItfklML+WRQEo0viPtrt3GIMY2+NU3FzkINbulwYbMCWoURgDNDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1734ceefae76c61a3521ffa39f3a7e553fc8f94a37d0952115e652e49cad7728","last_reissued_at":"2026-05-18T02:25:19.286821Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:25:19.286821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1503.02644","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":"2026-05-18T02:25:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6XNg9lWNMq5ErDQJDYe2lZIT8ZMAWslb4JmFkS4zjxbMk8N219CFj6tBSjCGkx6yc7O0iTNqGZqgv9jKv6ULDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T15:41:15.906578Z"},"content_sha256":"c85d818430d52cf886ff1600d20274415765878ecfb518a8b57bc53e695e33eb","schema_version":"1.0","event_id":"sha256:c85d818430d52cf886ff1600d20274415765878ecfb518a8b57bc53e695e33eb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:C42M535OO3DBUNJB76RZ6OT6KU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Jason Xu, Vladimir N. Minin","submitted_at":"2015-03-09T19:43:07Z","abstract_excerpt":"Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring a large integration step to impute over all possible hidden events. Recent work has successfully applied generating function techniq"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.02644","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":"2026-05-18T02:25:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ilmiMECQh/DR4p6e1khJCIK1EnDcUJlseVbOlv1u0OBRX96x2UnJC+YswGA80HyNAqYroSDveLzcijTKfsIeCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T15:41:15.907191Z"},"content_sha256":"a5fd666b56b3f4fb78f3964ce8d3c0bb9bc0f4f457638d1ab093dc9794135c95","schema_version":"1.0","event_id":"sha256:a5fd666b56b3f4fb78f3964ce8d3c0bb9bc0f4f457638d1ab093dc9794135c95"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C42M535OO3DBUNJB76RZ6OT6KU/bundle.json","state_url":"https://pith.science/pith/C42M535OO3DBUNJB76RZ6OT6KU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C42M535OO3DBUNJB76RZ6OT6KU/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":"2026-06-02T15:41:15Z","links":{"resolver":"https://pith.science/pith/C42M535OO3DBUNJB76RZ6OT6KU","bundle":"https://pith.science/pith/C42M535OO3DBUNJB76RZ6OT6KU/bundle.json","state":"https://pith.science/pith/C42M535OO3DBUNJB76RZ6OT6KU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C42M535OO3DBUNJB76RZ6OT6KU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:C42M535OO3DBUNJB76RZ6OT6KU","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":"c9838c462b7fdca9331dcc83e85d40c1aaa5737982750cd769b7728291df1ba8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-03-09T19:43:07Z","title_canon_sha256":"d236231bc2c212c9f97fea924a924044a6ec0b388adc017732c643f3201e665b"},"schema_version":"1.0","source":{"id":"1503.02644","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1503.02644","created_at":"2026-05-18T02:25:19Z"},{"alias_kind":"arxiv_version","alias_value":"1503.02644v1","created_at":"2026-05-18T02:25:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1503.02644","created_at":"2026-05-18T02:25:19Z"},{"alias_kind":"pith_short_12","alias_value":"C42M535OO3DB","created_at":"2026-05-18T12:29:14Z"},{"alias_kind":"pith_short_16","alias_value":"C42M535OO3DBUNJB","created_at":"2026-05-18T12:29:14Z"},{"alias_kind":"pith_short_8","alias_value":"C42M535O","created_at":"2026-05-18T12:29:14Z"}],"graph_snapshots":[{"event_id":"sha256:a5fd666b56b3f4fb78f3964ce8d3c0bb9bc0f4f457638d1ab093dc9794135c95","target":"graph","created_at":"2026-05-18T02:25:19Z","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":"Branching processes are a class of continuous-time Markov chains (CTMCs) with ubiquitous applications. A general difficulty in statistical inference under partially observed CTMC models arises in computing transition probabilities when the discrete state space is large or uncountable. Classical methods such as matrix exponentiation are infeasible for large or countably infinite state spaces, and sampling-based alternatives are computationally intensive, requiring a large integration step to impute over all possible hidden events. Recent work has successfully applied generating function techniq","authors_text":"Jason Xu, Vladimir N. Minin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-03-09T19:43:07Z","title":"Efficient Transition Probability Computation for Continuous-Time Branching Processes via Compressed Sensing"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1503.02644","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:c85d818430d52cf886ff1600d20274415765878ecfb518a8b57bc53e695e33eb","target":"record","created_at":"2026-05-18T02:25:19Z","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":"c9838c462b7fdca9331dcc83e85d40c1aaa5737982750cd769b7728291df1ba8","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2015-03-09T19:43:07Z","title_canon_sha256":"d236231bc2c212c9f97fea924a924044a6ec0b388adc017732c643f3201e665b"},"schema_version":"1.0","source":{"id":"1503.02644","kind":"arxiv","version":1}},"canonical_sha256":"1734ceefae76c61a3521ffa39f3a7e553fc8f94a37d0952115e652e49cad7728","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1734ceefae76c61a3521ffa39f3a7e553fc8f94a37d0952115e652e49cad7728","first_computed_at":"2026-05-18T02:25:19.286821Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:25:19.286821Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"dYB7xgDNi9f6RJK67wJVbN0tcvanpk5YqMItfklML+WRQEo0viPtrt3GIMY2+NU3FzkINbulwYbMCWoURgDNDA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:25:19.287169Z","signed_message":"canonical_sha256_bytes"},"source_id":"1503.02644","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c85d818430d52cf886ff1600d20274415765878ecfb518a8b57bc53e695e33eb","sha256:a5fd666b56b3f4fb78f3964ce8d3c0bb9bc0f4f457638d1ab093dc9794135c95"],"state_sha256":"584be2500d31235ea4f44ad8d4c4e1db79867e299865ea597218821ed6a38a99"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0JBzY8FdCQ7+xm7hGY6zNse9YpuYKoqVS5UbQ8N41Qo6J9BQRMAwtNIK9BartTcj0uCzyzMQA3uIQq7rz6o0BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T15:41:15.910107Z","bundle_sha256":"60704cb518838c46a46d6b2ce9cb8d5590fc5a1d731994ffab9c91668cf91325"}}