{"id":"W2547113758","doi":"10.1002/cjs.11323","title":"A comparative review of variable selection techniques for covariate dependent Dirichlet process mixture models","year":2017,"lang":"en","type":"review","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Covariate; Latent variable; Mathematics; Dirichlet distribution; Dirichlet process; Feature selection; Statistics; Econometrics; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008306213,0.001695513,0.002484243,0.004254746,0.0005020246,0.001845381,0.00370061,0.002071063,0.007136559],"category_scores_gemma":[0.02074618,0.001178543,0.002591513,0.008053184,0.001073894,0.002126938,0.001458501,0.002234807,0.003591346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001456983,"about_ca_system_score_gemma":0.001898755,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005475708,"about_ca_topic_score_gemma":0.004460198,"domain_scores_codex":[0.996277,0.001987779,0.0002828746,0.0005865991,0.0007763597,0.00008928608],"domain_scores_gemma":[0.9868826,0.01132402,0.0003036474,0.0003682597,0.001025521,0.00009596038],"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":[0.00006283644,0.00005754692,0.001041898,0.004970985,0.0003664022,0.0001023235,0.000257215,0.01459262,0.0003730323,0.06949996,0.01983579,0.8888394],"study_design_scores_gemma":[0.00009916472,0.000171002,0.004031817,0.007344033,0.000741472,0.001582835,0.0002820071,0.1049808,0.002130036,0.2273512,0.6510177,0.0002679581],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.001263555,0.5319591,0.4576165,0.002133435,0.0005095641,0.0001105409,0.0004810634,0.0005124974,0.005413855],"genre_scores_gemma":[0.0249435,0.6321977,0.3332287,0.0009980494,0.001616355,0.0005403439,0.001496089,0.0004718094,0.004507424],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.008306213,"threshold_uncertainty_score":0.04392803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1405629248767047,"score_gpt":0.3980014861420249,"score_spread":0.2574385612653201,"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."}}