{"id":"W7042764309","doi":"","title":"A Review of the Expectation-Maximization Algorithm and its Applications to Mixture Models","year":2023,"lang":"en","type":"dissertation","venue":"eScholarship@McGill (McGill)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Mixture model; Expectation–maximization algorithm; Pattern recognition (psychology); Feature (linguistics)","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":[0.004770463,0.001948248,0.001759751,0.002988931,0.0008571065,0.003162251,0.002296704,0.003036206,0.007758913],"category_scores_gemma":[0.01340013,0.001520299,0.001780103,0.007735297,0.002455209,0.004737976,0.001957807,0.00523166,0.007741154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002301555,"about_ca_system_score_gemma":0.002505636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004442018,"about_ca_topic_score_gemma":0.002759409,"domain_scores_codex":[0.9968619,0.001208333,0.0003164744,0.0004951314,0.001017193,0.0001009203],"domain_scores_gemma":[0.9948172,0.004032284,0.0001689006,0.0002419219,0.0006625138,0.00007709405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000463358,0.0001000732,0.0006430146,0.004598351,0.0001339826,0.000197914,0.0004805728,0.02082852,0.000864908,0.3006836,0.05613042,0.6152922],"study_design_scores_gemma":[0.00001583515,0.00007733488,0.001144856,0.002297795,0.00007420751,0.0007550197,0.0001158795,0.03117483,0.0009782867,0.237029,0.7262191,0.0001178465],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.000833747,0.4528211,0.5086565,0.004936399,0.002030765,0.0001083421,0.000326001,0.000588803,0.02969839],"genre_scores_gemma":[0.01506351,0.6195154,0.3449203,0.00221812,0.00529506,0.0003217768,0.000621854,0.0004996621,0.01154431],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.007758913,"threshold_uncertainty_score":0.02595615,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02258886843622614,"score_gpt":0.2787487058412923,"score_spread":0.2561598374050661,"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."}}