{"id":"W4388555695","doi":"10.1145/3632926","title":"Higher Order Bayesian Networks, Exactly","year":2024,"lang":"en","type":"article","venue":"Proceedings of the ACM on Programming Languages","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Leibniz-Gemeinschaft; Agence Nationale de la Recherche; Dalhousie University; European Commission; University of Cambridge","keywords":"Bayesian network; Rewriting; Computer science; Bayesian probability; Graphical model; Intersection (aeronautics); Probabilistic logic; Theoretical computer science; Inference; Bayesian inference; Semantics (computer science); Representation (politics); Variable-order Bayesian network; Algorithm; Mathematics; Programming language; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003749705,0.0001931153,0.0001694205,0.00009308589,0.0001106972,0.0005690769,0.002857389,0.0000956419,0.00001394439],"category_scores_gemma":[0.0002378541,0.0001247525,0.0001085382,0.0008772509,0.00008169214,0.0003477681,0.0007923226,0.0003258475,0.00001492824],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002769114,"about_ca_system_score_gemma":0.00003612734,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002440833,"about_ca_topic_score_gemma":9.671819e-7,"domain_scores_codex":[0.998625,0.000009532719,0.0002253922,0.0004376522,0.0003390742,0.0003633489],"domain_scores_gemma":[0.9990366,0.0000875566,0.00008895213,0.0005698301,0.0001436497,0.00007342402],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001154956,0.0001052088,0.0008492786,0.000261156,0.00008391865,0.00001022915,0.001347806,0.000118425,0.002034677,0.3720837,0.009007752,0.6140863],"study_design_scores_gemma":[0.001726346,0.002234922,0.007985331,0.008895501,0.0004371264,0.0003113073,0.001857892,0.3926727,0.08777509,0.28086,0.2107426,0.004501245],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2114801,0.0265844,0.5758911,0.1033237,0.008701709,0.003157086,0.00001423903,0.01051356,0.06033408],"genre_scores_gemma":[0.9340398,0.00002417276,0.06394408,0.0003460681,0.0001925981,0.00003195923,5.674284e-7,0.0000241359,0.001396636],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7225597,"threshold_uncertainty_score":0.5487622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01729227918575449,"score_gpt":0.2720950399917865,"score_spread":0.254802760806032,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}