{"as_of":"2026-08-16T07:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:c01756ec0fea0762b4aa6e17e0d009124563b7bcbdc95b43442a77ca4ee563c2","coverage":[{"denominator":57,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":57,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T14:59:10.860072Z","state":"measured"},{"denominator":57,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":57,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.02062/citation-record","integrity":"/paper/1908.02062/integrity","json":"/paper/1908.02062/citation-record.json","paper":"/paper/1908.02062"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.898395Z","title":"Abadi, A","venue":null,"work_id":"2184b884-95c4-4785-982f-49ad244ab203","year":2015},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.612414Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:a230b59dcac24109f6306738ff4229270f2949a2945094d16741333cc66ad5b7","observation_id":"b3a06d29-60b4-43b4-9fb9-0d72c84eb396","resolution":{"observed_at":"2026-08-14T14:59:11.904402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.881709Z","title":"Apache Hadoop , 2018 a","venue":null,"work_id":"24f31dc0-6942-46b2-97c8-58f526183aaa","year":2018},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.617666Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:4fbfc07506984e712d2a023e34c18c8cc633d4b908f284c20733207a08341061","observation_id":"936c524b-7ee1-4141-8a84-d69a6d2d6616","resolution":{"observed_at":"2026-08-14T14:59:11.886665Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.865741Z","title":"Apache Spark - Unified Analytics Engine for Big Data , 2018 b","venue":null,"work_id":"6da28f3d-1a41-495e-9a28-ac38a003efcb","year":2018},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.621842Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:1648c780e86e69b0e31a346bad56327c2a50bac0f75a1263fd082b3b6663104e","observation_id":"ea131622-2d57-47b7-b6a2-7ba2cdbac858","resolution":{"observed_at":"2026-08-14T14:59:11.871163Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.849443Z","title":null,"venue":null,"work_id":"85d73aba-d5fb-4fc6-ac8c-8c1092708964","year":2005},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.627994Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:6cb773d4ddd0b89752fc24e4f2689f6d87899a93aa8cb020ea86b1fdae6cc7d8","observation_id":"d5e614b8-8dc8-4f80-9a81-6c564c20627a","resolution":{"observed_at":"2026-08-14T14:59:11.854383Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.833942Z","title":null,"venue":null,"work_id":"da8fed4b-091a-4289-8efe-8e36f844668b","year":2010},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.632096Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:005e43510897ffb782235d466a602ddbfcdf8bf6f5730a81121413f302aca8b4","observation_id":"f7594a4e-88c0-4970-ba9d-2ca19979063b","resolution":{"observed_at":"2026-08-14T14:59:11.838824Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.819277Z","title":"Barr and C","venue":null,"work_id":"dff7be00-2364-4613-912b-5ca3784e0155","year":1990},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.636138Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:ba9c24b42e17ccec293290fba93baf6d0350351a64a165df89f17b411e19b7bd","observation_id":"bdcf7e56-ba45-4151-a66b-26fbafd18da6","resolution":{"observed_at":"2026-08-14T14:59:11.823835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1701.02434","last_updated":"2018-07-16T02:46:10Z","snapshot_observed_at":"2026-08-14T21:21:54.681306Z","submitted_at":"2017-01-10T04:26:06Z","title":"A Conceptual Introduction to Hamiltonian Monte Carlo","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.02434","snapshot_observed_at":"2026-08-14T14:59:10.640870Z","title":"Betancourt","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.640870Z"},"links":{"cited_paper":"/paper/1701.02434","citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:461aa07f1c8aba00cf335e92f55318b4adc7ca9c1b3ac52fecacaf16c52ec010","observation_id":"a489e95b-1a1f-4019-8918-d55317357989","resolution":{"observed_at":"2026-08-14T14:59:10.640870Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:10.645229Z","title":null,"venue":null,"work_id":null,"year":1958},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.645229Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:c1a3aef964ccd031752c3e59b1bb47cde1d2c757c97a17f5ec61549c8c227985","observation_id":"f3dabe1d-2d87-48e7-853d-c9f2430c419b","resolution":{"observed_at":"2026-08-14T14:59:10.645229Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:10.649549Z","title":"Brin and L","venue":null,"work_id":null,"year":1998},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.649549Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:88341c2ef3b4a1643b4793708bc227c3db15725484203cd7da3ddacb9d68ad01","observation_id":"a76d012f-8cad-4b53-9cb6-262d2f3892d7","resolution":{"observed_at":"2026-08-14T14:59:10.649549Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.794115Z","title":null,"venue":null,"work_id":"7a46a994-2a6e-4343-8a2f-0af905beaf01","year":2018},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.654277Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:de59b5937812fdc0a2aaf9c1513e1d6fc55c313727ee926589e75462262c0f4d","observation_id":"bf16cbd9-c2d1-40a0-8f21-a5c931e1fb16","resolution":{"observed_at":"2026-08-14T14:59:11.800325Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.779786Z","title":"Carpenter, A","venue":null,"work_id":"d3cbf1c9-eaa3-45eb-8bb1-e21832b81df2","year":2016},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.658706Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:3f2a50c3c136fecea77218afb9f171cedffbd5eff183f6286e935fad45fb78ba","observation_id":"541c89ed-63ca-487c-90b5-408d6d864beb","resolution":{"observed_at":"2026-08-14T14:59:11.784679Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.765967Z","title":"Chiusano and R","venue":null,"work_id":"bc32dbef-f445-4e47-9071-2b378c8cd3fa","year":2014},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.663082Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:bb8e25e357831377af68d64b81363b11f700fcdcef5a0602b83ca3922bd9e3b1","observation_id":"327248d9-96d6-4722-a9f7-919d5a02a9e8","resolution":{"observed_at":"2026-08-14T14:59:11.770338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.751143Z","title":"Culbertson and K","venue":null,"work_id":"7666bce2-18e6-44a5-9fe2-5cbbcde14464","year":2014},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.667334Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:3ffc75726a80290362ba684e4edc65ef2e8a7f9d2528172dd015871a5f38cf61","observation_id":"e4d904fc-9006-4ba4-91f1-47ea96d4a4d3","resolution":{"observed_at":"2026-08-14T14:59:11.755824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.735280Z","title":"Dean and S","venue":null,"work_id":"69f58c58-703b-49ff-8732-241da84eee58","year":2008},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.671913Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:e115a815d1371a97ae027497f60420ab7a73601c42d5d20da0bd6764399270e5","observation_id":"3bc3f8f9-2383-4f12-9296-92954f0f56ee","resolution":{"observed_at":"2026-08-14T14:59:11.740185Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.719931Z","title":"Duane, A","venue":null,"work_id":"3dfdf296-bf61-4bef-bff1-d21c0d45aa13","year":1987},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.676177Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:6f1c729adab819d493c66d4577e88f9c30e070f22519b83cea886241ad5a317f","observation_id":"e93a715e-1af2-4cc6-93c3-c770a5dcc644","resolution":{"observed_at":"2026-08-14T14:59:11.724577Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:10.680882Z","title":null,"venue":null,"work_id":null,"year":1960},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.680882Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:9794e8282c9fe697111a4dc54832ee5803dac9109b716d34e3a3bccb6568da33","observation_id":"1fea9a78-5cfc-4074-8478-04e42b01744e","resolution":{"observed_at":"2026-08-14T14:59:10.680882Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.05363","last_updated":"2019-03-12T13:25:48Z","snapshot_observed_at":"2026-08-15T10:58:51.652133Z","submitted_at":"2017-12-14T17:48:42Z","title":"A Probability Monad as the Colimit of Spaces of Finite Samples","version":4},"cited_work":{"arxiv_id":"1712.05363","doi":null,"metadata_source":"pith","pith_arxiv_id":"1712.05363","snapshot_observed_at":"2026-08-14T14:59:11.050649Z","title":"A Probability Monad as the Colimit of Spaces of Finite Samples","venue":"math.PR","work_id":"1ea94242-524e-4a9f-9991-7e0f1a6e1d2d","year":2017},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.686118Z"},"links":{"cited_paper":"/paper/1712.05363","citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:27032750fff312961203a259405248d6f9c81ed18a94a5415dc0e2731eef4332","observation_id":"6562416b-69e1-4efe-aa8f-e81ac4189cdf","resolution":{"observed_at":"2026-08-14T14:59:11.055735Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.705486Z","title":null,"venue":null,"work_id":"a80310ad-7168-4247-8aac-99c807ae17ce","year":1982},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.690740Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:63c775afe923720c99bc9d696a0d6939ae49fc0f9426e937ad3273a2ee33a2ff","observation_id":"9b93ea6b-ab10-40ca-81c7-a3aabfa604ca","resolution":{"observed_at":"2026-08-14T14:59:11.710343Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.690781Z","title":null,"venue":null,"work_id":"40a8f80c-b6f8-4c98-8d83-bed50d105cf2","year":1970},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.695101Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:94e64274016872e5e3ea216ea42cfba35076028d584e80ac1a7f52b99ef94b2a","observation_id":"67cb8bfa-0740-4a2c-8e78-eee40048c2b3","resolution":{"observed_at":"2026-08-14T14:59:11.695455Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.677331Z","title":"Heunen, O","venue":null,"work_id":"32098bd4-eace-4713-b908-20165b891805","year":2017},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.699623Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:c61e12593b0269ea7063c5d87484736e8426787c2660883c0723fb7ca5feb030","observation_id":"8f34a353-c094-48d6-90fa-b3c8aa21a920","resolution":{"observed_at":"2026-08-14T14:59:11.681662Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.662204Z","title":null,"venue":null,"work_id":"600e1c42-0ec0-45ad-aa96-483600d52bf1","year":2014},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.704036Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:af5016a6f00f21dac90e4caf08b0e3e5c367bc06559e1b6b97a6fe7d0a990614","observation_id":"58505d00-e7ee-4346-a1cc-e9a307e6a91e","resolution":{"observed_at":"2026-08-14T14:59:11.667430Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:10.709158Z","title":"Huang, Z","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.709158Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:47efeccf6b993fbc010e5e95bfac9611bc581553562e8eb44e14dbc84394806b","observation_id":"09270ea0-9ca0-45de-8ac9-8709a9b90e42","resolution":{"observed_at":"2026-08-14T14:59:10.709158Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.638670Z","title":null,"venue":null,"work_id":"5297136a-90b7-446e-b053-65b45835e16a","year":2018},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.714408Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:f67255c1995b0ee3c6ac5ce29c2b2224944214ffbb5ae50755224aacddfef1b4","observation_id":"980c0013-ba70-465d-9ccc-6ec8a4029316","resolution":{"observed_at":"2026-08-14T14:59:11.643366Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.623917Z","title":null,"venue":null,"work_id":"0afe3e79-2572-4a7f-851c-11618def39fa","year":1993},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.718657Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:80296ab4f2766fc7bc1ed01daead59615e3cd2ff5f8b8fa538f82b5baa93f584","observation_id":"dda53c68-7877-4d25-8777-fda5216ae5fc","resolution":{"observed_at":"2026-08-14T14:59:11.628391Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.609896Z","title":null,"venue":null,"work_id":"17cbadad-42eb-4e88-b1e2-63307010cb71","year":2016},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.722630Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:de83dbbc2032f197d18b8e4fe17427b30824b17d896bcb5b324373c19ed62a9a","observation_id":"e7b52c22-6465-4e84-afb5-8fcdb8be8f01","resolution":{"observed_at":"2026-08-14T14:59:11.614446Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.594944Z","title":"Kiselyov, A","venue":null,"work_id":"c9e42ab5-bd3f-45c2-be53-c52beb343c16","year":2013},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.726622Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:4c9b4f5690b4bbb046611a672a736c9c587868ffb57d7901bca93c8dfceeccb7","observation_id":"98db791c-ea43-4aa0-af47-73a350d5b30a","resolution":{"observed_at":"2026-08-14T14:59:11.600066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.580102Z","title":"Kucukelbir, D","venue":null,"work_id":"36842205-55ca-45b4-950e-a327e90548e9","year":2017},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.730371Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:c0eb7d57146e35294e551ee5062c4738df483228dd2a260708b60cb65513820e","observation_id":"7a23d935-c018-43f1-9982-3751ec1123cc","resolution":{"observed_at":"2026-08-14T14:59:11.584680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.565501Z","title":null,"venue":null,"work_id":"3d108a99-b0fa-4db8-a9b1-a5858dc7e552","year":1962},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.734564Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:f8a96615a8fa9f01da632b3bbe44ac9c1cba91c02fe68ef0608f093c6f973e73","observation_id":"044eef54-be35-4a3b-8213-3d6509d05fa4","resolution":{"observed_at":"2026-08-14T14:59:11.570677Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.550796Z","title":"Liang, P","venue":null,"work_id":"da554e62-bb6f-4813-9abb-b09f6e014eb9","year":1995},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.738483Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:86c9862b7b26e12148357a0e6746f0609731fad3d3a2f02f6d157f7e3bb5e45a","observation_id":"3ec782d0-8b1d-45fc-b61e-52e4be8a8b7f","resolution":{"observed_at":"2026-08-14T14:59:11.555581Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.536281Z","title":null,"venue":null,"work_id":"d5ec88ed-b475-40fe-9b46-7a3d2f8f447f","year":2000},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.742242Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:784d378b93782eab0e0b707f021315ff9b0e8fe36d28ee139741e67618e0ca30","observation_id":"75b48fe8-fa26-47e9-a2c1-27fa4ee105a8","resolution":{"observed_at":"2026-08-14T14:59:11.541267Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.522406Z","title":null,"venue":null,"work_id":"a0562d8d-0abb-4964-a910-c62c150f9b25","year":2012},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.746502Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:cbce91dd82ea2c9a33f03bb2947100b896f3abf0193224c864b70797add71d92","observation_id":"3e28a1cb-0db4-46ac-a2c1-4fc8c481c4d5","resolution":{"observed_at":"2026-08-14T14:59:11.526726Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.508710Z","title":"Matsumoto and T","venue":null,"work_id":"b37fe739-c46d-47e1-b02a-11e46703fa48","year":1998},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.751635Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:8dda9cb14d510079dd3dffbc4cacdb3a0c434c47f70e59680d48ff4d90e8c774","observation_id":"c3673d13-7199-444c-8e24-f63ad2e1ff90","resolution":{"observed_at":"2026-08-14T14:59:11.512829Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.494671Z","title":"McBride and R","venue":null,"work_id":"3517578f-510b-422c-86b8-afe490c87ec0","year":2008},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.756012Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:86b033e6de8f07191378d15d068846d806d0f5fac51a184e903b1d142bd3d101","observation_id":"06979d4e-c7a5-4bcd-921f-449b35b75345","resolution":{"observed_at":"2026-08-14T14:59:11.499114Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.480799Z","title":"Metropolis, A","venue":null,"work_id":"bc0c9d27-5a2b-4e1a-b805-c21da33c81b4","year":1953},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.760504Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:245ccc4815be512ff0204b1e61eff8dbebfce11852c63bc3a7b99d9ae0ae2adf","observation_id":"dc947c50-e8c5-4fd9-8b5e-5969a86458b3","resolution":{"observed_at":"2026-08-14T14:59:11.485412Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.464483Z","title":"Milewski","venue":null,"work_id":"9705b46c-a68c-4541-9e64-9245d0d368b0","year":2018},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.764798Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:43082b3e2f877476749732ed28fd4b652ad8991c57f086337258fac68adf2fdb","observation_id":"350914ac-3ee3-4dfa-b312-085572dd64a4","resolution":{"observed_at":"2026-08-14T14:59:11.469361Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-319-29604-3_5","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:10.890109Z","title":"Narayanan, J","venue":null,"work_id":"7118226d-856c-463c-9417-3fc5dbc92321","year":2016},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.769340Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:d66ae32a7cc765e442345866305dab0aa63b015c131e3e3ddea66f6170f6acda","observation_id":"530a8472-366f-4df6-aaaa-ec5ecee5eb57","resolution":{"observed_at":"2026-08-14T14:59:10.895913Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.448760Z","title":null,"venue":null,"work_id":"3f3b4d1b-02fe-4aeb-b54e-8e8c93bcd236","year":2011},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.773860Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:f8f4bc4a7a509b92df364a9abbcb00282e47331c92b8bc7fb40c41c344bb9c99","observation_id":"a5a63cfb-158a-4f6f-be4d-77d34342ac4b","resolution":{"observed_at":"2026-08-14T14:59:11.453715Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.433207Z","title":"Odersky, P","venue":null,"work_id":"d3d4b1f2-a0fc-422f-b980-796ae2114bfb","year":2004},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.778308Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:6fe9215f39d5f081275fb4f38fab0782194a1dfdcf506db2dc555ae4d8106ca5","observation_id":"45c3de9a-1cc1-470d-9620-edbb89cf1c5c","resolution":{"observed_at":"2026-08-14T14:59:11.438615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.418796Z","title":null,"venue":null,"work_id":"0d5d726c-bd9e-4e04-ba33-0f3571671d57","year":2005},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.782376Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:e33d4f906dcc000922691b3b3a26ae42fec2709e197a4b747ad47a2059660a51","observation_id":"ff1af1d8-c100-42fd-9941-4f1ef98538c3","resolution":{"observed_at":"2026-08-14T14:59:11.423285Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.403922Z","title":"Paszke, S","venue":null,"work_id":"fff56bda-2604-4aa0-abb4-34d798d7cccc","year":2017},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.786665Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:ae09af57edba025b04b16f6471c6f610ba39a2119aee219ffa92fd843d1d391d","observation_id":"d4d7aff7-1a04-4e99-b6c4-f5ccc3772c7d","resolution":{"observed_at":"2026-08-14T14:59:11.408761Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.389425Z","title":"Plummer et al","venue":null,"work_id":"c761e512-7a4b-404a-aa36-16c7917b0a19","year":2003},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.791062Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:04f7b0430e3250bd51bc2a68cf99ab0b86b72258ab0ef85551cf4ca5fa983c8f","observation_id":"a7498189-423c-4e84-a8f8-0459a79c97bd","resolution":{"observed_at":"2026-08-14T14:59:11.393479Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:10.795525Z","title":"R: A Language and Environment for Statistical Computing","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.795525Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:583e9b3db28b79adc5bb9b44e0b5f3a3b16e56a71b8150dd037c336315eaede0","observation_id":"49130214-42ff-4dba-9bb1-16498ca1e756","resolution":{"observed_at":"2026-08-14T14:59:10.795525Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.367323Z","title":"Ramsey and A","venue":null,"work_id":"17d4fac3-a827-4cc2-b147-e6b0c80c0525","year":2002},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.799747Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:61ce500bab45886affdf566a8883ded82c3ee7ed9851b73e59614f7e1010add1","observation_id":"e8a882dc-650b-4477-b89b-256480a4ff13","resolution":{"observed_at":"2026-08-14T14:59:11.371473Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.353968Z","title":null,"venue":null,"work_id":"b593523b-c706-4937-8343-4ae8157535f3","year":1998},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.804047Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:53e01755ab87b29e6c513c2d2f6bfcb6851c549118535534c62114c1a7d0705f","observation_id":"1adf8663-10b8-45ee-a05e-3dd577d9501e","resolution":{"observed_at":"2026-08-14T14:59:11.358281Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.340040Z","title":null,"venue":null,"work_id":"5fbbb10e-47ad-4d09-a1a8-f1011517308f","year":1997},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.808498Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:818bde570975914091b12eacd37c4cbc6c31ffd5d6dc9bf4871156318b0f7f4c","observation_id":"2bb3ad00-f774-4e8d-b287-76f3361ced04","resolution":{"observed_at":"2026-08-14T14:59:11.344479Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.326354Z","title":"\\'S cibior, Z","venue":null,"work_id":"0f45aeea-58c8-43b5-b429-6c30ce518e23","year":2015},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.812923Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:fb5e2f59d21435042063b6db5e00c1441fdce064cae7c519f40a07596086786a","observation_id":"f9b44c78-e6f1-49b9-874a-e357a07ff811","resolution":{"observed_at":"2026-08-14T14:59:11.330820Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.312319Z","title":"\\'S cibior, O","venue":null,"work_id":"7d067153-5e47-4750-996f-33eab10a25f4","year":2018},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.817174Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:56426e58b670940e9fcaac6557b1f14d97cdba7fcd67c11408b54993fa2bac82","observation_id":"112dbaea-4387-487e-a940-eeea74821268","resolution":{"observed_at":"2026-08-14T14:59:11.316804Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.296634Z","title":"\\'S cibior, O","venue":null,"work_id":"e4cc376a-3963-4f04-bbe5-5f01aa596112","year":2018},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.821598Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:7388cbd0792fd366228dfb34f965da9dfda7151f21f9dbf62bdb8b8aad9c961c","observation_id":"b177667f-4f83-4f16-91a5-2fd4eac32be2","resolution":{"observed_at":"2026-08-14T14:59:11.301556Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.281964Z","title":null,"venue":null,"work_id":"2656a417-3b21-4b19-af08-7c20b0e8bc54","year":2017},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.825799Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:252e593e0b96c470cca0ec1adc5e42063e3942bceeaf03756701823c1bfb49c0","observation_id":"ce06393f-724a-4789-b707-e3a400f6d61d","resolution":{"observed_at":"2026-08-14T14:59:11.286026Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.268121Z","title":"Swierstra","venue":null,"work_id":"f07d6442-e7e6-41e1-bb2f-c784fe618892","year":2008},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":50,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.830241Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:afb60e9a18a4de7e7a7a524ac47d980f7adee7fc75a610d3dffc68c749949491","observation_id":"7b17650f-45dc-48b2-8cbd-27cdf9a907e0","resolution":{"observed_at":"2026-08-14T14:59:11.272505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.255257Z","title":"Tensorflow probability, 2018","venue":null,"work_id":"9be27f5b-5845-4d47-a4af-07ebef3b936a","year":2018},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":51,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.834497Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:0087e2e8200ffd889a604c26817c91e8b82e89a9e15d3ab8a7fa1fc5e5ea9585","observation_id":"1e8ce678-aa32-444c-bb0f-d001f523061f","resolution":{"observed_at":"2026-08-14T14:59:11.259270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.240824Z","title":"Pyro: Deep universal probabilistic programming with Python and PyTorch , 2018","venue":null,"work_id":"a1f29030-0fc7-42ce-a54d-0d8d9f77ed40","year":2018},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":52,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.838931Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:a08b75bd66484966412518b2c1ee1fe1b3863fe7580fb0fa8111fbc666959606","observation_id":"36703105-4c2d-4002-8303-8770215d9fc1","resolution":{"observed_at":"2026-08-14T14:59:11.245441Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.226808Z","title":null,"venue":null,"work_id":"e143bba2-b66a-4422-a1ae-8d2343267515","year":1995},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":53,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.843195Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:2b682fb6488ec04f26793df89b229160d417cfec576cd3dee3ce0a0c970df62a","observation_id":"dca1c0b3-6b69-4193-91f6-e0caa88cdc15","resolution":{"observed_at":"2026-08-14T14:59:11.231334Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1803.10228","last_updated":"2019-08-29T03:09:03Z","snapshot_observed_at":"2026-08-14T19:32:11.553855Z","submitted_at":"2018-03-27T04:43:12Z","title":"Demystifying Differentiable Programming: Shift/Reset the Penultimate Backpropagator","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1803.10228","snapshot_observed_at":"2026-08-14T14:59:10.847486Z","title":null,"venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":54,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.847486Z"},"links":{"cited_paper":"/paper/1803.10228","citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:62f82cb02a0d064883fc9c7e8526e3adf8d62b8f07b38224948c634bc9cc6733","observation_id":"3655c81e-a0de-4b68-9e0c-b87f8d794a6f","resolution":{"observed_at":"2026-08-14T14:59:10.847486Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":"slideslive.ch/3890879","doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:11.009714Z","title":null,"venue":null,"work_id":"93d3d5fc-6b97-4170-9b5d-0afd095db680","year":2018},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":55,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.852110Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:b2d0673dd86908226fb5eae4ddea9d95f0d3d83bee2b9e5753f7c98d7ae8ed2e","observation_id":"e0e7aeb7-36ab-4be1-9853-cf35a23cad7d","resolution":{"observed_at":"2026-08-14T14:59:11.017367Z","resolver_source":"raw_fallback","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:59:10.855934Z","title":null,"venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":56,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.855934Z"},"links":{"citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:0112749934bf9456e7a3ced7b4773acb8232de8bb1040363ddd9f31131263628","observation_id":"c4d79e10-6f6f-4730-ad0c-5e716b79ef15","resolution":{"observed_at":"2026-08-14T14:59:10.855934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04449","last_updated":"2026-05-22T11:03:41Z","snapshot_observed_at":"2026-08-16T03:23:35.902062Z","submitted_at":"2018-10-10T10:34:48Z","title":"Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale: a self-calibrated randomized solution","version":3},"cited_work":{"arxiv_id":"1810.04449","doi":null,"metadata_source":"pith","pith_arxiv_id":"1810.04449","snapshot_observed_at":"2026-08-14T14:59:10.911558Z","title":"Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale: a self-calibrated randomized solution","venue":"stat.CO","work_id":"62479908-6be8-417f-a5af-9a446d4c0cb2","year":2018},"citing_paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling","version":1},"reference_index":57,"source":"arxiv_source","source_observed_at":"2026-08-14T14:59:10.860072Z"},"links":{"cited_paper":"/paper/1810.04449","citing_paper":"/paper/1908.02062"},"observation_digest":"sha256:8704a8b9ecd8090b8f868f6a66577c5a063d2ebf80e055ed83a0ad3767aeca45","observation_id":"c9483947-0a75-4bd8-8be6-555c24eb883f","resolution":{"observed_at":"2026-08-14T14:59:10.917404Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.02062","last_updated":"2019-08-06T10:31:34Z","latest_version":1,"primary_category":"stat.CO","snapshot_observed_at":"2026-08-16T04:42:25.289074Z","submitted_at":"2019-08-06T10:31:34Z","title":"Functional probabilistic programming for scalable Bayesian modelling"},"reference_resolution":{"displayed":57,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":26,"verified_exact":4,"verified_fuzzy":27},"total_outbound_references":57},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 57 of 57 outbound references and 0 inbound Pith citation observations for arXiv:1908.02062."}