{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:5CA2Z5GCACTNTJNVNDVCFRBOAO","short_pith_number":"pith:5CA2Z5GC","canonical_record":{"source":{"id":"1403.6036","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-03-24T16:51:06Z","cross_cats_sorted":[],"title_canon_sha256":"a33698e85ebaa400e90b71898b902f9a0dff45e89f8b3838f8a67595537dcd2c","abstract_canon_sha256":"ff81020b220333a4ae5ae40388a9717dd45fde8085a8bf06516fe5743225b0ee"},"schema_version":"1.0"},"canonical_sha256":"e881acf4c200a6d9a5b568ea22c42e039248836934c75458e208cb8e70d37888","source":{"kind":"arxiv","id":"1403.6036","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1403.6036","created_at":"2026-05-18T02:55:47Z"},{"alias_kind":"arxiv_version","alias_value":"1403.6036v1","created_at":"2026-05-18T02:55:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1403.6036","created_at":"2026-05-18T02:55:47Z"},{"alias_kind":"pith_short_12","alias_value":"5CA2Z5GCACTN","created_at":"2026-05-18T12:28:14Z"},{"alias_kind":"pith_short_16","alias_value":"5CA2Z5GCACTNTJNV","created_at":"2026-05-18T12:28:14Z"},{"alias_kind":"pith_short_8","alias_value":"5CA2Z5GC","created_at":"2026-05-18T12:28:14Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:5CA2Z5GCACTNTJNVNDVCFRBOAO","target":"record","payload":{"canonical_record":{"source":{"id":"1403.6036","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-03-24T16:51:06Z","cross_cats_sorted":[],"title_canon_sha256":"a33698e85ebaa400e90b71898b902f9a0dff45e89f8b3838f8a67595537dcd2c","abstract_canon_sha256":"ff81020b220333a4ae5ae40388a9717dd45fde8085a8bf06516fe5743225b0ee"},"schema_version":"1.0"},"canonical_sha256":"e881acf4c200a6d9a5b568ea22c42e039248836934c75458e208cb8e70d37888","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:55:47.094883Z","signature_b64":"lXB8l/eGmfGTHxqqFR2jU/iilMDHAThRf4y8q1YUXomIjXxZUCSE4Xong49YUgBlw1urJG23oUJ5aVwkDvliBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e881acf4c200a6d9a5b568ea22c42e039248836934c75458e208cb8e70d37888","last_reissued_at":"2026-05-18T02:55:47.094027Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:55:47.094027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1403.6036","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":"2026-05-18T02:55:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZAdBlhgqv1rj9sod0F4Ku3fyt6FDfV5LFGEWA3Cnfi02VRZEl3lAPSS0MxDkxjBl+4o8ujcqH9V4GbmniUaJBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T06:36:14.491998Z"},"content_sha256":"7f7c9d3d9c4a88372576aacc5d4bb2ba82bdddace57d0af1554ab97b2cf4a875","schema_version":"1.0","event_id":"sha256:7f7c9d3d9c4a88372576aacc5d4bb2ba82bdddace57d0af1554ab97b2cf4a875"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:5CA2Z5GCACTNTJNVNDVCFRBOAO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Adaptive MCMC-Based Inference in Probabilistic Logic Programs","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.AI","authors_text":"Arun Nampally, C. R. Ramakrishnan","submitted_at":"2014-03-24T16:51:06Z","abstract_excerpt":"Probabilistic Logic Programming (PLP) languages enable programmers to specify systems that combine logical models with statistical knowledge. The inference problem, to determine the probability of query answers in PLP, is intractable in general, thereby motivating the need for approximate techniques. In this paper, we present a technique for approximate inference of conditional probabilities for PLP queries. It is an Adaptive Markov Chain Monte Carlo (MCMC) technique, where the distribution from which samples are drawn is modified as the Markov Chain is explored. In particular, the distributio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1403.6036","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":"2026-05-18T02:55:47Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k1t6mu1UKTCujFR0TFc9o1L6s5eVksiwHeGqte2b3NP76bD32BRGownIpViodZkD/AAH4uxOEuoa0qAn+LVbBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-30T06:36:14.492362Z"},"content_sha256":"34c1b801021a56aa4da045690b8675759ac8fe753289058c51abb4f3f96d7dab","schema_version":"1.0","event_id":"sha256:34c1b801021a56aa4da045690b8675759ac8fe753289058c51abb4f3f96d7dab"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5CA2Z5GCACTNTJNVNDVCFRBOAO/bundle.json","state_url":"https://pith.science/pith/5CA2Z5GCACTNTJNVNDVCFRBOAO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5CA2Z5GCACTNTJNVNDVCFRBOAO/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":"2026-06-30T06:36:14Z","links":{"resolver":"https://pith.science/pith/5CA2Z5GCACTNTJNVNDVCFRBOAO","bundle":"https://pith.science/pith/5CA2Z5GCACTNTJNVNDVCFRBOAO/bundle.json","state":"https://pith.science/pith/5CA2Z5GCACTNTJNVNDVCFRBOAO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5CA2Z5GCACTNTJNVNDVCFRBOAO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:5CA2Z5GCACTNTJNVNDVCFRBOAO","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":"ff81020b220333a4ae5ae40388a9717dd45fde8085a8bf06516fe5743225b0ee","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-03-24T16:51:06Z","title_canon_sha256":"a33698e85ebaa400e90b71898b902f9a0dff45e89f8b3838f8a67595537dcd2c"},"schema_version":"1.0","source":{"id":"1403.6036","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1403.6036","created_at":"2026-05-18T02:55:47Z"},{"alias_kind":"arxiv_version","alias_value":"1403.6036v1","created_at":"2026-05-18T02:55:47Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1403.6036","created_at":"2026-05-18T02:55:47Z"},{"alias_kind":"pith_short_12","alias_value":"5CA2Z5GCACTN","created_at":"2026-05-18T12:28:14Z"},{"alias_kind":"pith_short_16","alias_value":"5CA2Z5GCACTNTJNV","created_at":"2026-05-18T12:28:14Z"},{"alias_kind":"pith_short_8","alias_value":"5CA2Z5GC","created_at":"2026-05-18T12:28:14Z"}],"graph_snapshots":[{"event_id":"sha256:34c1b801021a56aa4da045690b8675759ac8fe753289058c51abb4f3f96d7dab","target":"graph","created_at":"2026-05-18T02:55:47Z","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":"Probabilistic Logic Programming (PLP) languages enable programmers to specify systems that combine logical models with statistical knowledge. The inference problem, to determine the probability of query answers in PLP, is intractable in general, thereby motivating the need for approximate techniques. In this paper, we present a technique for approximate inference of conditional probabilities for PLP queries. It is an Adaptive Markov Chain Monte Carlo (MCMC) technique, where the distribution from which samples are drawn is modified as the Markov Chain is explored. In particular, the distributio","authors_text":"Arun Nampally, C. R. Ramakrishnan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-03-24T16:51:06Z","title":"Adaptive MCMC-Based Inference in Probabilistic Logic Programs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1403.6036","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":"sha256:7f7c9d3d9c4a88372576aacc5d4bb2ba82bdddace57d0af1554ab97b2cf4a875","target":"record","created_at":"2026-05-18T02:55:47Z","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":"ff81020b220333a4ae5ae40388a9717dd45fde8085a8bf06516fe5743225b0ee","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2014-03-24T16:51:06Z","title_canon_sha256":"a33698e85ebaa400e90b71898b902f9a0dff45e89f8b3838f8a67595537dcd2c"},"schema_version":"1.0","source":{"id":"1403.6036","kind":"arxiv","version":1}},"canonical_sha256":"e881acf4c200a6d9a5b568ea22c42e039248836934c75458e208cb8e70d37888","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e881acf4c200a6d9a5b568ea22c42e039248836934c75458e208cb8e70d37888","first_computed_at":"2026-05-18T02:55:47.094027Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:55:47.094027Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lXB8l/eGmfGTHxqqFR2jU/iilMDHAThRf4y8q1YUXomIjXxZUCSE4Xong49YUgBlw1urJG23oUJ5aVwkDvliBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:55:47.094883Z","signed_message":"canonical_sha256_bytes"},"source_id":"1403.6036","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7f7c9d3d9c4a88372576aacc5d4bb2ba82bdddace57d0af1554ab97b2cf4a875","sha256:34c1b801021a56aa4da045690b8675759ac8fe753289058c51abb4f3f96d7dab"],"state_sha256":"ca5a24fc85d0542bfba821ced05ef910cbd91bbc698f25e001f63000c64b2905"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DLo/zmE1lUJydsInZsB3K7UojJVLDY7AVbty7Uz7P8cNkaUFOqQeFwIeuU8EguLwXGj+kHnKEAJzGstPDODJDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-30T06:36:14.494183Z","bundle_sha256":"e98d90df4863fb2688a15e951b662b398757a0e08cd02186d8d5fa39e9fdbbce"}}