{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:B7MD4C4CDN7DNRYYKMKZU6PCJQ","short_pith_number":"pith:B7MD4C4C","canonical_record":{"source":{"id":"1507.04228","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2015-07-15T14:16:27Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"57b84f30d44ce699e836adf9052721a94641d7a5adec86af12f327a68807b7ec","abstract_canon_sha256":"95c68a9cd21d2461f624b3f4dbd5656e629986a272317f02249fdf68818c5a11"},"schema_version":"1.0"},"canonical_sha256":"0fd83e0b821b7e36c71853159a79e24c2f9b3bbd203c815d1ed9d1cb1385d136","source":{"kind":"arxiv","id":"1507.04228","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.04228","created_at":"2026-05-18T01:20:26Z"},{"alias_kind":"arxiv_version","alias_value":"1507.04228v3","created_at":"2026-05-18T01:20:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.04228","created_at":"2026-05-18T01:20:26Z"},{"alias_kind":"pith_short_12","alias_value":"B7MD4C4CDN7D","created_at":"2026-05-18T12:29:14Z"},{"alias_kind":"pith_short_16","alias_value":"B7MD4C4CDN7DNRYY","created_at":"2026-05-18T12:29:14Z"},{"alias_kind":"pith_short_8","alias_value":"B7MD4C4C","created_at":"2026-05-18T12:29:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:B7MD4C4CDN7DNRYYKMKZU6PCJQ","target":"record","payload":{"canonical_record":{"source":{"id":"1507.04228","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2015-07-15T14:16:27Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"57b84f30d44ce699e836adf9052721a94641d7a5adec86af12f327a68807b7ec","abstract_canon_sha256":"95c68a9cd21d2461f624b3f4dbd5656e629986a272317f02249fdf68818c5a11"},"schema_version":"1.0"},"canonical_sha256":"0fd83e0b821b7e36c71853159a79e24c2f9b3bbd203c815d1ed9d1cb1385d136","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:26.113212Z","signature_b64":"wFDVCkh3gqKNPS1WjsaqCYTE4LXJ3PGdBYRe6rT/dG1wmeL3oN1r7+I6Cr4Ud+VwB8W3b0v8dyqFMb+Jj1FKCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0fd83e0b821b7e36c71853159a79e24c2f9b3bbd203c815d1ed9d1cb1385d136","last_reissued_at":"2026-05-18T01:20:26.112592Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:26.112592Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1507.04228","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":"2026-05-18T01:20:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Z8TtPko2oqK+TJ8kXa0LrpxKNXWwZshZRfW/SLcPDVyTaeIBujlLFBQg6m+pTrJ8HRc1MqOfodutRBpwrSbvBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T01:41:37.408385Z"},"content_sha256":"057ad6be0c514bbb3eb39505c6b2f07359bb302e01f897037e48bf3ffd742389","schema_version":"1.0","event_id":"sha256:057ad6be0c514bbb3eb39505c6b2f07359bb302e01f897037e48bf3ffd742389"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:B7MD4C4CDN7DNRYYKMKZU6PCJQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ABC Shadow algorithm: a tool for statistical analysis of spatial patterns","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"A. Philippe, J. Mateu, P. Gregori, R. S. Stoica","submitted_at":"2015-07-15T14:16:27Z","abstract_excerpt":"This paper presents an original ABC algorithm, \"ABC Shadow\", that can be applied to sample posterior densities that are continuously differentiable. The proposed method uses the ideas given by the auxiliary variable MH of (M\\o ller and Waagepetersen, 2004). The obtained algorithm solves the main condition to be fulfilled by any ABC algorithm, in order to be useful in practice. This condition requires enough samples in the parameter space region, induced by the observed statistics (Blum, 2010). The algorithm is tuned on the posterior of a Gaussian model which is entirely known, and then it is a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.04228","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":"2026-05-18T01:20:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HeOXKaUMFWMZfr0t5MBsriNSxAEYM96qGxx6ekID5y/ATNZ/RVtz5nbiTb8N8BhksbmKoVH0bEdtGRcLmp3TBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-27T01:41:37.408729Z"},"content_sha256":"2324bc81984854059024ac914a3f82a10637b325b0c22b9e88340eaa700d3626","schema_version":"1.0","event_id":"sha256:2324bc81984854059024ac914a3f82a10637b325b0c22b9e88340eaa700d3626"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/B7MD4C4CDN7DNRYYKMKZU6PCJQ/bundle.json","state_url":"https://pith.science/pith/B7MD4C4CDN7DNRYYKMKZU6PCJQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/B7MD4C4CDN7DNRYYKMKZU6PCJQ/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":"2026-06-27T01:41:37Z","links":{"resolver":"https://pith.science/pith/B7MD4C4CDN7DNRYYKMKZU6PCJQ","bundle":"https://pith.science/pith/B7MD4C4CDN7DNRYYKMKZU6PCJQ/bundle.json","state":"https://pith.science/pith/B7MD4C4CDN7DNRYYKMKZU6PCJQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/B7MD4C4CDN7DNRYYKMKZU6PCJQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:B7MD4C4CDN7DNRYYKMKZU6PCJQ","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":"95c68a9cd21d2461f624b3f4dbd5656e629986a272317f02249fdf68818c5a11","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2015-07-15T14:16:27Z","title_canon_sha256":"57b84f30d44ce699e836adf9052721a94641d7a5adec86af12f327a68807b7ec"},"schema_version":"1.0","source":{"id":"1507.04228","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1507.04228","created_at":"2026-05-18T01:20:26Z"},{"alias_kind":"arxiv_version","alias_value":"1507.04228v3","created_at":"2026-05-18T01:20:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1507.04228","created_at":"2026-05-18T01:20:26Z"},{"alias_kind":"pith_short_12","alias_value":"B7MD4C4CDN7D","created_at":"2026-05-18T12:29:14Z"},{"alias_kind":"pith_short_16","alias_value":"B7MD4C4CDN7DNRYY","created_at":"2026-05-18T12:29:14Z"},{"alias_kind":"pith_short_8","alias_value":"B7MD4C4C","created_at":"2026-05-18T12:29:14Z"}],"graph_snapshots":[{"event_id":"sha256:2324bc81984854059024ac914a3f82a10637b325b0c22b9e88340eaa700d3626","target":"graph","created_at":"2026-05-18T01:20:26Z","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 an original ABC algorithm, \"ABC Shadow\", that can be applied to sample posterior densities that are continuously differentiable. The proposed method uses the ideas given by the auxiliary variable MH of (M\\o ller and Waagepetersen, 2004). The obtained algorithm solves the main condition to be fulfilled by any ABC algorithm, in order to be useful in practice. This condition requires enough samples in the parameter space region, induced by the observed statistics (Blum, 2010). The algorithm is tuned on the posterior of a Gaussian model which is entirely known, and then it is a","authors_text":"A. Philippe, J. Mateu, P. Gregori, R. S. Stoica","cross_cats":["stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2015-07-15T14:16:27Z","title":"ABC Shadow algorithm: a tool for statistical analysis of spatial patterns"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1507.04228","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":"sha256:057ad6be0c514bbb3eb39505c6b2f07359bb302e01f897037e48bf3ffd742389","target":"record","created_at":"2026-05-18T01:20:26Z","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":"95c68a9cd21d2461f624b3f4dbd5656e629986a272317f02249fdf68818c5a11","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2015-07-15T14:16:27Z","title_canon_sha256":"57b84f30d44ce699e836adf9052721a94641d7a5adec86af12f327a68807b7ec"},"schema_version":"1.0","source":{"id":"1507.04228","kind":"arxiv","version":3}},"canonical_sha256":"0fd83e0b821b7e36c71853159a79e24c2f9b3bbd203c815d1ed9d1cb1385d136","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0fd83e0b821b7e36c71853159a79e24c2f9b3bbd203c815d1ed9d1cb1385d136","first_computed_at":"2026-05-18T01:20:26.112592Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:20:26.112592Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"wFDVCkh3gqKNPS1WjsaqCYTE4LXJ3PGdBYRe6rT/dG1wmeL3oN1r7+I6Cr4Ud+VwB8W3b0v8dyqFMb+Jj1FKCg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:20:26.113212Z","signed_message":"canonical_sha256_bytes"},"source_id":"1507.04228","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:057ad6be0c514bbb3eb39505c6b2f07359bb302e01f897037e48bf3ffd742389","sha256:2324bc81984854059024ac914a3f82a10637b325b0c22b9e88340eaa700d3626"],"state_sha256":"860f10793321a03dbebae50ff0415cae52cd9b21394bb462948d7c8cbc5fb6b1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"O92h3pHUr5biwNSdMT2oWO8APIhuTyW2AgflLp5PipU1oJfn8JIu67qBaDfQ0GUG98Nqab9Sy6t20s9YebKzAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-27T01:41:37.410640Z","bundle_sha256":"47b17973a2c04c604a703ac257c4fb2c8ed3eda2d0b33263e338c2c3df72c0d9"}}