{"id":"W3139051417","doi":"10.1111/exsy.12688","title":"Multivariate‐bounded Gaussian mixture model with minimum message length criterion for model selection","year":2021,"lang":"en","type":"article","venue":"Expert Systems","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; MNIST database; Cluster analysis; Model selection; Mixture model; Selection (genetic algorithm); Artificial intelligence; Representation (politics); Pattern recognition (psychology); Feature selection; Bounded function; Data mining; Gaussian; Machine learning; Artificial neural network; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004377234,0.0003293863,0.0004443023,0.0001007081,0.0003227693,0.0004587076,0.0004567994,0.000239052,0.000003442228],"category_scores_gemma":[0.00003302675,0.0002588662,0.0001333811,0.0003342336,0.00002769139,0.000586042,0.00009350599,0.0001838436,0.000003625325],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001143643,"about_ca_system_score_gemma":0.0003100178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008382546,"about_ca_topic_score_gemma":0.00002995268,"domain_scores_codex":[0.9976712,0.000229141,0.0003756356,0.0008700868,0.0003577677,0.0004961541],"domain_scores_gemma":[0.9985945,0.00007628317,0.0001485998,0.0006912954,0.0003095529,0.0001797926],"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":[0.0002380271,0.0005624153,0.00001726194,0.0004630692,0.0002903854,0.00007389529,0.02183857,0.03790186,0.2582801,0.6450145,0.02066228,0.01465756],"study_design_scores_gemma":[0.0008498073,0.00008571825,0.000002474023,0.0001286797,0.00001265854,0.000102513,0.00008411452,0.9826835,0.007399121,0.006233048,0.002036779,0.000381545],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0007462288,0.0009690169,0.994485,0.000949379,0.0005991122,0.0005921389,0.00001385376,0.0002521628,0.001393059],"genre_scores_gemma":[0.2851742,0.00001818564,0.7094002,0.0003752053,0.0002221118,0.0002880703,0.00001426313,0.00004185229,0.004465964],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9447817,"threshold_uncertainty_score":0.9999864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03003543556985333,"score_gpt":0.29848945313278,"score_spread":0.2684540175629267,"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."}}