{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002298879,0.0003803715,0.002761887,0.0003822403,0.0002085791,0.0001851621,0.001663414,0.0002622817,0.00001422032],"category_scores_gemma":[0.0004843401,0.0002978845,0.0002948904,0.0003030608,0.00007685601,0.0003738723,0.00003234211,0.0005819858,7.564241e-7],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002474748,"about_ca_system_score_gemma":0.009311498,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000274165,"about_ca_topic_score_gemma":0.00048622,"domain_scores_codex":[0.9971314,0.0003732497,0.001474731,0.0003189492,0.0003353949,0.0003662508],"domain_scores_gemma":[0.9930114,0.0003340403,0.0035143,0.0005080872,0.00217336,0.000458848],"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":[0.00000251412,0.00001673059,1.18631e-7,0.05823259,0.0002265731,0.0000351669,0.0001515983,0.000008575666,1.690313e-7,0.1626427,0.01994612,0.7587371],"study_design_scores_gemma":[0.0001128461,0.0002141175,1.094533e-7,0.0748503,0.0008517171,0.0004629327,0.000001595578,0.002191514,0.00000659187,0.1467113,0.7742506,0.0003462871],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[1.165708e-9,0.4875613,0.5107002,0.00002352523,0.0001983822,0.0005174346,0.0006914366,0.000003723493,0.0003039463],"genre_scores_gemma":[5.718443e-7,0.501983,0.4977048,0.00007374737,0.00008508821,0.00002618928,0.00001915935,0.0000155249,0.00009191004],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7583908,"threshold_uncertainty_score":0.9999473,"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."}}