{"id":"W3125744459","doi":"10.48550/arxiv.1703.08882","title":"Finite Mixtures of Skewed Matrix Variate Distributions","year":2017,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Random variate; Cluster analysis; Expectation–maximization algorithm; Mathematics; Data Matrix; Statistics; Matrix (chemical analysis); Multivariate statistics; Multivariate normal distribution; Applied mathematics; Computer science; Random variable; Maximum likelihood; Materials science","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":[0.005238904,0.000879362,0.001580992,0.001981674,0.001178549,0.003356079,0.002571774,0.002302404,0.005040115],"category_scores_gemma":[0.02411959,0.000787173,0.001431075,0.002428374,0.002523556,0.003663782,0.002356487,0.002396931,0.001668096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001679102,"about_ca_system_score_gemma":0.00107247,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003598764,"about_ca_topic_score_gemma":0.003271233,"domain_scores_codex":[0.9960602,0.00175609,0.0001557435,0.0008068979,0.000933055,0.0002879728],"domain_scores_gemma":[0.9866855,0.009322366,0.00111209,0.0013026,0.001268441,0.0003089999],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002437593,0.00007326949,0.005803343,0.0001972483,0.0001465775,0.0004000212,0.0005363561,0.3189538,0.003047167,0.6086659,0.003800568,0.05813207],"study_design_scores_gemma":[0.0000226705,0.00002925871,0.001508601,0.00005701305,0.00002946106,0.0002687409,0.00009587561,0.6987072,0.0009723543,0.2944457,0.003797153,0.00006610603],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01861622,0.0004524717,0.9774953,0.0003139556,0.00003771772,0.00008094504,0.0002350343,0.0003191333,0.0024492],"genre_scores_gemma":[0.6042253,0.001454058,0.3820413,0.0004407521,0.0001910333,0.0005372827,0.001500471,0.0002914483,0.009318281],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005238904,"threshold_uncertainty_score":0.02770633,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0684987053061779,"score_gpt":0.2335793429708089,"score_spread":0.165080637664631,"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."}}