{"id":"W2969211293","doi":"10.1002/sta4.243","title":"A bootstrap‐augmented alternating expectation‐conditional maximization algorithm for mixtures of factor analyzers","year":2019,"lang":"en","type":"article","venue":"Stat","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Latent variable; Expectation–maximization algorithm; Cluster analysis; Curse of dimensionality; Benchmark (surveying); Computer science; Nonparametric statistics; Maximization; Latent variable model; Mixture model; Algorithm; Factor (programming language); Artificial intelligence; Machine learning; Mathematics; Statistics; Mathematical optimization; Maximum likelihood","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005746082,0.001429231,0.001671686,0.0015596,0.0008163926,0.001531433,0.003090791,0.001933197,0.004595031],"category_scores_gemma":[0.01847073,0.001055974,0.001502609,0.001880124,0.001299891,0.002690067,0.003216411,0.003152158,0.0030354],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007520209,"about_ca_system_score_gemma":0.002333097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003099544,"about_ca_topic_score_gemma":0.004988005,"domain_scores_codex":[0.9968675,0.001740239,0.0001570574,0.0004323443,0.0006396698,0.0001630894],"domain_scores_gemma":[0.9939157,0.004248788,0.0003256637,0.0005580459,0.0007537508,0.0001979621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0006073741,0.0002550159,0.001817313,0.0002664243,0.0002807464,0.0002193165,0.0003470556,0.3661525,0.01051551,0.1125035,0.0105636,0.4964717],"study_design_scores_gemma":[0.00002503643,0.00002142646,0.0001155588,0.0000103779,0.00001102182,0.00004225364,0.000009429194,0.9738067,0.0009165361,0.02344842,0.001577053,0.00001610299],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001129418,0.00005285499,0.9981585,0.000053416,0.00001177496,0.00002606008,0.00002425506,0.0003146284,0.0002291474],"genre_scores_gemma":[0.04090546,0.0001058786,0.9567984,0.000111272,0.00004986711,0.0002290805,0.0003802711,0.0002743918,0.001145383],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005746082,"threshold_uncertainty_score":0.03038853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02134522745326021,"score_gpt":0.300813821875367,"score_spread":0.2794685944221068,"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."}}