{"id":"W3008182123","doi":"10.1109/ssci44817.2019.9002852","title":"Variational Inference of Finite Generalized Gaussian Mixture Models","year":2019,"lang":"en","type":"article","venue":"","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Expectation–maximization algorithm; Mixture model; Computer science; Gaussian; Inference; Estimator; Artificial intelligence; Posterior probability; Algorithm; Prior probability; Image segmentation; Generative model; Pattern recognition (psychology); Mathematical optimization; Image (mathematics); Mathematics; Maximum likelihood; Generative grammar; Bayesian probability; Statistics","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":[0.004818989,0.001125409,0.001845096,0.002430958,0.0007660474,0.001997221,0.003266615,0.002011967,0.001792378],"category_scores_gemma":[0.01378512,0.001367969,0.002252116,0.001568158,0.00226022,0.002516756,0.002005849,0.002710937,0.0003657251],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002009457,"about_ca_system_score_gemma":0.001855777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01446005,"about_ca_topic_score_gemma":0.01249739,"domain_scores_codex":[0.9977739,0.00124341,0.00007919294,0.0003359639,0.0004215959,0.000145961],"domain_scores_gemma":[0.994699,0.00428462,0.0003039618,0.0002289137,0.0003517354,0.0001319079],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003914297,0.0000209538,0.0006433685,0.00007488361,0.0001251129,0.0000622246,0.000101735,0.810252,0.0008442253,0.1681771,0.0008068375,0.01885238],"study_design_scores_gemma":[0.000002773058,0.000003540219,0.00005915708,0.000006376583,0.000004703789,0.000007480227,0.000004862433,0.9694931,0.00009155396,0.03004326,0.0002763868,0.000006763979],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002728506,0.0002148889,0.9964116,0.00009927388,0.00001416023,0.00001189692,0.00003461377,0.00007998721,0.00040514],"genre_scores_gemma":[0.3704048,0.001223703,0.6209493,0.000257178,0.0001767816,0.0002956543,0.0007962905,0.0004036372,0.00549275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01446005,"threshold_uncertainty_score":0.02875179,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02257159305778399,"score_gpt":0.2702057833013048,"score_spread":0.2476341902435208,"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."}}