{"id":"W2796204195","doi":"10.1007/978-3-319-89656-4_1","title":"Compressing Bayesian Networks: Swarm-Based Descent, Efficiency, and Posterior Accuracy","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; Bayesian network; Inference; Generality; Tree (set theory); Simple (philosophy); Artificial intelligence; Bayesian inference; Range (aeronautics); Algorithm; Bayesian probability; Machine learning; Mathematics","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":[0.004888599,0.001297651,0.002144744,0.001296967,0.0006349343,0.00195348,0.00223057,0.002385832,0.003130772],"category_scores_gemma":[0.03494072,0.001135908,0.0009396041,0.001795208,0.001793496,0.004358981,0.002562982,0.003698903,0.0006201643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001513892,"about_ca_system_score_gemma":0.001753966,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00657773,"about_ca_topic_score_gemma":0.005032239,"domain_scores_codex":[0.9987425,0.000569443,0.00008655388,0.0001767192,0.0003407963,0.00008399176],"domain_scores_gemma":[0.9845653,0.01266073,0.000519345,0.0009962227,0.0009899134,0.000268447],"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":[0.0001441685,0.00005962302,0.0009503996,0.0001818218,0.00006453675,0.00004799217,0.0001677765,0.7962747,0.0008479616,0.0874509,0.0037668,0.1100433],"study_design_scores_gemma":[0.00000952099,0.00001412423,0.00007344524,0.00001516886,0.00000571038,0.00001260785,0.000006157752,0.9729581,0.0001615463,0.02639806,0.0003412164,0.000004346416],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0124432,0.0006993009,0.9838996,0.0004894843,0.00005870169,0.00004105871,0.0000849092,0.0002411916,0.002042489],"genre_scores_gemma":[0.3393762,0.002118088,0.6497638,0.0003204118,0.0004134354,0.0003770588,0.000754612,0.0005191634,0.006357218],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00657773,"threshold_uncertainty_score":0.02585369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02192291859564915,"score_gpt":0.259994383729386,"score_spread":0.2380714651337368,"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."}}