{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:SJXD27IQATZ6JA2R72INNUQD3O","short_pith_number":"pith:SJXD27IQ","canonical_record":{"source":{"id":"1310.0973","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2013-10-03T13:23:57Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"38565bd5954673bceabae9972cd9a7a0dd8bbb2770c7295c80b50d85d731eaeb","abstract_canon_sha256":"fb6a10d92876c798ebe09feb72e942575fe29ee968ed7fffd67d1e100e4aa5af"},"schema_version":"1.0"},"canonical_sha256":"926e3d7d1004f3e48351fe90d6d203dbbd983b0982fcf76220fa4e8040517813","source":{"kind":"arxiv","id":"1310.0973","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1310.0973","created_at":"2026-05-18T02:30:40Z"},{"alias_kind":"arxiv_version","alias_value":"1310.0973v3","created_at":"2026-05-18T02:30:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1310.0973","created_at":"2026-05-18T02:30:40Z"},{"alias_kind":"pith_short_12","alias_value":"SJXD27IQATZ6","created_at":"2026-05-18T12:27:59Z"},{"alias_kind":"pith_short_16","alias_value":"SJXD27IQATZ6JA2R","created_at":"2026-05-18T12:27:59Z"},{"alias_kind":"pith_short_8","alias_value":"SJXD27IQ","created_at":"2026-05-18T12:27:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:SJXD27IQATZ6JA2R72INNUQD3O","target":"record","payload":{"canonical_record":{"source":{"id":"1310.0973","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2013-10-03T13:23:57Z","cross_cats_sorted":["stat.AP"],"title_canon_sha256":"38565bd5954673bceabae9972cd9a7a0dd8bbb2770c7295c80b50d85d731eaeb","abstract_canon_sha256":"fb6a10d92876c798ebe09feb72e942575fe29ee968ed7fffd67d1e100e4aa5af"},"schema_version":"1.0"},"canonical_sha256":"926e3d7d1004f3e48351fe90d6d203dbbd983b0982fcf76220fa4e8040517813","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:30:40.947797Z","signature_b64":"ahhd/rexG0IW3XNIWbb5ShufncK9joLV42d1hUc0hCFqbTwI5jXZOOc+sEQo2WNXK85v4sHbXRvHgdgYINtSAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"926e3d7d1004f3e48351fe90d6d203dbbd983b0982fcf76220fa4e8040517813","last_reissued_at":"2026-05-18T02:30:40.947168Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:30:40.947168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1310.0973","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":"2026-05-18T02:30:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jKucCLjV8TEA2RTBu5Q5o3qazdeMYqQl6BxmOvqkZXKcph0XLgDb7XAFG8juvcuXMKPDCw5qKPHq9y7xapBpBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T16:39:44.647205Z"},"content_sha256":"1539d95e99e3f7a742f5995b7be97e18184df98ef2b25b4448388fb4a0c953ed","schema_version":"1.0","event_id":"sha256:1539d95e99e3f7a742f5995b7be97e18184df98ef2b25b4448388fb4a0c953ed"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:SJXD27IQATZ6JA2R72INNUQD3O","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Accelerating inference for diffusions observed with measurement error and large sample sizes using Approximate Bayesian Computation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.AP"],"primary_cat":"stat.CO","authors_text":"Julie Lyng Forman, Umberto Picchini","submitted_at":"2013-10-03T13:23:57Z","abstract_excerpt":"In recent years dynamical modelling has been provided with a range of breakthrough methods to perform exact Bayesian inference. However it is often computationally unfeasible to apply exact statistical methodologies in the context of large datasets and complex models. This paper considers a nonlinear stochastic differential equation model observed with correlated measurement errors and an application to protein folding modelling. An Approximate Bayesian Computation (ABC) MCMC algorithm is suggested to allow inference for model parameters within reasonable time constraints. The ABC algorithm us"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1310.0973","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":"2026-05-18T02:30:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iYIZLJvDc9omPPwyfUdwpJWADpetnGdrpicVKk8k5+mey5d1mMWSlPLZg6d/zrUYkOzVUbHzyxjJRqTiKJp8CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T16:39:44.647901Z"},"content_sha256":"552e8920929e014468d7cb04f28187f0645392ccfd781162921151ad63182966","schema_version":"1.0","event_id":"sha256:552e8920929e014468d7cb04f28187f0645392ccfd781162921151ad63182966"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SJXD27IQATZ6JA2R72INNUQD3O/bundle.json","state_url":"https://pith.science/pith/SJXD27IQATZ6JA2R72INNUQD3O/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SJXD27IQATZ6JA2R72INNUQD3O/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":"2026-05-27T16:39:44Z","links":{"resolver":"https://pith.science/pith/SJXD27IQATZ6JA2R72INNUQD3O","bundle":"https://pith.science/pith/SJXD27IQATZ6JA2R72INNUQD3O/bundle.json","state":"https://pith.science/pith/SJXD27IQATZ6JA2R72INNUQD3O/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SJXD27IQATZ6JA2R72INNUQD3O/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:SJXD27IQATZ6JA2R72INNUQD3O","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":"fb6a10d92876c798ebe09feb72e942575fe29ee968ed7fffd67d1e100e4aa5af","cross_cats_sorted":["stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2013-10-03T13:23:57Z","title_canon_sha256":"38565bd5954673bceabae9972cd9a7a0dd8bbb2770c7295c80b50d85d731eaeb"},"schema_version":"1.0","source":{"id":"1310.0973","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1310.0973","created_at":"2026-05-18T02:30:40Z"},{"alias_kind":"arxiv_version","alias_value":"1310.0973v3","created_at":"2026-05-18T02:30:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1310.0973","created_at":"2026-05-18T02:30:40Z"},{"alias_kind":"pith_short_12","alias_value":"SJXD27IQATZ6","created_at":"2026-05-18T12:27:59Z"},{"alias_kind":"pith_short_16","alias_value":"SJXD27IQATZ6JA2R","created_at":"2026-05-18T12:27:59Z"},{"alias_kind":"pith_short_8","alias_value":"SJXD27IQ","created_at":"2026-05-18T12:27:59Z"}],"graph_snapshots":[{"event_id":"sha256:552e8920929e014468d7cb04f28187f0645392ccfd781162921151ad63182966","target":"graph","created_at":"2026-05-18T02:30:40Z","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":"In recent years dynamical modelling has been provided with a range of breakthrough methods to perform exact Bayesian inference. However it is often computationally unfeasible to apply exact statistical methodologies in the context of large datasets and complex models. This paper considers a nonlinear stochastic differential equation model observed with correlated measurement errors and an application to protein folding modelling. An Approximate Bayesian Computation (ABC) MCMC algorithm is suggested to allow inference for model parameters within reasonable time constraints. The ABC algorithm us","authors_text":"Julie Lyng Forman, Umberto Picchini","cross_cats":["stat.AP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2013-10-03T13:23:57Z","title":"Accelerating inference for diffusions observed with measurement error and large sample sizes using Approximate Bayesian Computation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1310.0973","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":"sha256:1539d95e99e3f7a742f5995b7be97e18184df98ef2b25b4448388fb4a0c953ed","target":"record","created_at":"2026-05-18T02:30:40Z","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":"fb6a10d92876c798ebe09feb72e942575fe29ee968ed7fffd67d1e100e4aa5af","cross_cats_sorted":["stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2013-10-03T13:23:57Z","title_canon_sha256":"38565bd5954673bceabae9972cd9a7a0dd8bbb2770c7295c80b50d85d731eaeb"},"schema_version":"1.0","source":{"id":"1310.0973","kind":"arxiv","version":3}},"canonical_sha256":"926e3d7d1004f3e48351fe90d6d203dbbd983b0982fcf76220fa4e8040517813","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"926e3d7d1004f3e48351fe90d6d203dbbd983b0982fcf76220fa4e8040517813","first_computed_at":"2026-05-18T02:30:40.947168Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:30:40.947168Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ahhd/rexG0IW3XNIWbb5ShufncK9joLV42d1hUc0hCFqbTwI5jXZOOc+sEQo2WNXK85v4sHbXRvHgdgYINtSAA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:30:40.947797Z","signed_message":"canonical_sha256_bytes"},"source_id":"1310.0973","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1539d95e99e3f7a742f5995b7be97e18184df98ef2b25b4448388fb4a0c953ed","sha256:552e8920929e014468d7cb04f28187f0645392ccfd781162921151ad63182966"],"state_sha256":"e33947b1e16cdbbf7492d28ab2c3a3724069e30f253d2f5ae830f0df7680a170"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CoPcjpP9Gse6qAUNW8xnRJai/C0BW8twPudY2zBHE288EtjgDrHLDpfaO3dt9Qmx0b7UkZyzn4y+Y5IDwwmHDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T16:39:44.651530Z","bundle_sha256":"057ea320e84f46dacd549b4de6696818225a4a8abca70fb894e44e23b224841a"}}