{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003050136,0.000103265,0.0001339797,0.00008519064,0.00007071075,0.00003959015,0.000192947,0.00008159441,0.000003836934],"category_scores_gemma":[0.0005920889,0.0000991161,0.00006341557,0.0002489756,0.0000294223,0.0004002827,0.00002335822,0.0000614571,0.000002505119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002269165,"about_ca_system_score_gemma":0.0000646694,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004924095,"about_ca_topic_score_gemma":0.000002064632,"domain_scores_codex":[0.9989932,0.00006715754,0.000303041,0.0002755776,0.0001981875,0.0001627978],"domain_scores_gemma":[0.9984205,0.0007586955,0.0001159529,0.0003065843,0.0003437157,0.00005457509],"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":[0.00000503916,0.00003750729,0.00006161092,0.00001870182,0.000003121536,7.39777e-8,0.0001640835,0.7677334,0.00008990282,0.2279543,8.061655e-7,0.003931453],"study_design_scores_gemma":[0.0002150461,0.00005561949,0.0001132534,0.00001525353,0.000005374297,9.566281e-8,0.000002713901,0.7487346,0.0002071643,0.2505379,0.00002776391,0.00008524972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02336841,0.00002604133,0.9758511,0.00002953429,0.000106454,0.0003442873,0.000002800549,0.00007414685,0.0001972212],"genre_scores_gemma":[0.9444957,0.000001331304,0.05541223,0.00003207508,0.00001270361,0.00002048788,0.000004961805,0.000006208048,0.00001430547],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9211273,"threshold_uncertainty_score":0.4041837,"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."}}