{"id":"W2163438293","doi":"10.1177/0037549703039950","title":"Simulation of Graphical Models for Multiagent Probabilistic Inference","year":2003,"lang":"en","type":"article","venue":"SIMULATION","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of Waterloo; University of Guelph","funders":"","keywords":"Computer science; Bayesian network; Graphical model; Backtracking; Probabilistic logic; Inference; Domain (mathematical analysis); Set (abstract data type); Machine learning; Artificial intelligence; Influence diagram; Theoretical computer science; Data mining; Algorithm; Programming language; Mathematics; Decision tree","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002358986,0.0008691882,0.0009376662,0.000806884,0.0007124392,0.001489924,0.001541731,0.001382624,0.006325181],"category_scores_gemma":[0.01354619,0.0007212033,0.001145367,0.0007842373,0.00135237,0.002002441,0.001845977,0.001856501,0.0007455276],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001756408,"about_ca_system_score_gemma":0.001336837,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005515422,"about_ca_topic_score_gemma":0.004917272,"domain_scores_codex":[0.997712,0.001405384,0.0000989368,0.0002348393,0.0004621533,0.00008664328],"domain_scores_gemma":[0.9884875,0.009603951,0.0003481649,0.0009518568,0.0004464459,0.0001620954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008652384,0.00004820331,0.0004841021,0.00005897652,0.00004265509,0.0000487597,0.00006570339,0.8960316,0.0009481309,0.0787048,0.000815728,0.02266479],"study_design_scores_gemma":[0.00001150208,0.000007177159,0.00002609276,0.000005094078,0.000003629182,0.00001022292,0.000004211222,0.9686728,0.0004078332,0.03011387,0.0007336592,0.000003819985],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009969937,0.000125187,0.9864945,0.0002009372,0.00002421819,0.00007016499,0.0000698865,0.0008609754,0.002184151],"genre_scores_gemma":[0.406516,0.0002929225,0.5900817,0.0001357845,0.0000229788,0.0003995442,0.0003840282,0.0001717977,0.001995338],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006325181,"threshold_uncertainty_score":0.02115983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07601483875247786,"score_gpt":0.3319199079838257,"score_spread":0.2559050692313478,"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."}}