{"id":"W133532789","doi":"","title":"Unsupervised Selection and Estimation of Non-Gaussian Mixtures for High Dimensional Data Analysis","year":2014,"lang":"en","type":"dissertation","venue":"Spectrum Research Repository (Concordia University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Mixture model; Gaussian; Computer science; Artificial intelligence; Generalized inverse Gaussian distribution; Gaussian process; Machine learning; Model selection; Density estimation; Probability distribution; Algorithm; Data mining; Pattern recognition (psychology); Gaussian random field; Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001261903,0.0002644636,0.0005897881,0.001979493,0.0005491163,0.0001778758,0.001511897,0.0003552179,0.000005719332],"category_scores_gemma":[0.0001121981,0.0002741357,0.0001666251,0.002051923,0.0001052057,0.0005941223,0.0003440303,0.0004942844,0.000001060769],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001230878,"about_ca_system_score_gemma":0.0006304649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005822088,"about_ca_topic_score_gemma":0.005654583,"domain_scores_codex":[0.9968448,0.0005972973,0.0003240631,0.001144255,0.0006522243,0.0004374029],"domain_scores_gemma":[0.9974298,0.0004809095,0.0002903035,0.00119709,0.0003884829,0.0002133451],"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":[0.003913001,0.001016118,0.008477121,0.00422565,0.01027124,0.0003238157,0.002833524,0.004109574,0.1147018,0.5972623,0.005865266,0.2470006],"study_design_scores_gemma":[0.001225412,0.0007158657,0.05445633,0.0001728538,0.001041684,0.00001119147,0.00006874179,0.8645735,0.05915616,0.01710499,0.0008030144,0.0006702649],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1214355,0.00008680518,0.8745643,0.0001685497,0.0003659014,0.0005364962,0.00003491336,0.00005017126,0.002757358],"genre_scores_gemma":[0.9062763,0.0000381351,0.08675674,0.00000600698,0.0001537807,0.000005007414,0.0006934493,0.00002393573,0.006046652],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8604639,"threshold_uncertainty_score":0.9999711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02660127392471069,"score_gpt":0.3081239003071691,"score_spread":0.2815226263824584,"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."}}