{"id":"W2963716487","doi":"","title":"Maximum margin Bayesian networks","year":2005,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Alberta","funders":"","keywords":"Graphical model; Margin (machine learning); Bayesian network; Computer science; Variable-order Bayesian network; Markov blanket; Artificial intelligence; Network topology; Discriminative model; Generalization; Machine learning; Bayesian probability; Normalization (sociology); Dynamic Bayesian network; Markov chain; Mathematical optimization; Markov model; Bayesian inference; Mathematics; Variable-order Markov model","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":[0.0001638672,0.0001587417,0.0001369416,0.0001019586,0.0001545816,0.00009350396,0.001040005,0.0001039969,0.00006665301],"category_scores_gemma":[0.000006841221,0.0001773594,0.0000841486,0.0005936504,0.00005670641,0.0006455844,0.0002337206,0.0002131439,0.0002440984],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007525886,"about_ca_system_score_gemma":0.00004997319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002806,"about_ca_topic_score_gemma":0.00002995507,"domain_scores_codex":[0.9988275,0.00006108706,0.0001216,0.0005462621,0.00005582556,0.0003877865],"domain_scores_gemma":[0.9990105,0.00004143845,0.00006016555,0.0006320196,0.00006202823,0.0001938279],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001380208,0.00006845811,0.0009006329,0.000003901859,0.00001877865,0.0001092337,0.00008607625,0.4069553,0.00002615711,0.564749,0.001362592,0.02570605],"study_design_scores_gemma":[0.0002466778,0.00003760126,0.0002606685,0.00001166494,0.000008301605,0.000008717226,0.00001245339,0.9770437,0.0000489203,0.01981976,0.002273039,0.0002284588],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01356349,0.00005353486,0.9770109,0.0004368349,0.0001534264,0.00006199632,7.607324e-7,0.0002912063,0.008427826],"genre_scores_gemma":[0.9887506,0.00005563544,0.008628923,0.0004829686,0.0001097468,2.599575e-7,0.000001494655,0.000009143024,0.001961235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9751871,"threshold_uncertainty_score":0.7232506,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04537573185943207,"score_gpt":0.1730638119909792,"score_spread":0.1276880801315471,"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."}}