{"id":"W2096936651","doi":"10.1002/cjs.5550360308","title":"Nonparametric adaptive likelihood weights","year":2008,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Mathematics; Nonparametric statistics; Maximization; Statistics; Convergence (economics); Expectation–maximization algorithm; Population; Entropy (arrow of time); Maximum likelihood; Applied mathematics; Computer science; Mathematical optimization; Artificial intelligence; Demography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.006338567,0.000908414,0.001252862,0.001794001,0.0006701886,0.002215922,0.003190832,0.001946322,0.006348979],"category_scores_gemma":[0.04757566,0.0007449452,0.000886752,0.002068941,0.002260144,0.004099641,0.00274892,0.002721615,0.001151492],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001518166,"about_ca_system_score_gemma":0.0009626055,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002139743,"about_ca_topic_score_gemma":0.001269026,"domain_scores_codex":[0.9951352,0.00294874,0.0001792605,0.0006419219,0.0009365629,0.0001583153],"domain_scores_gemma":[0.9877586,0.008664321,0.0006602315,0.001370829,0.001350396,0.0001956252],"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":[0.0001894351,0.00006167851,0.001601598,0.0001287714,0.0001269432,0.000117317,0.0001457119,0.3613121,0.00194638,0.4868306,0.003144394,0.1443951],"study_design_scores_gemma":[0.00003300942,0.00002692336,0.0004395144,0.00003469414,0.00001819417,0.00008084119,0.00002154936,0.7807419,0.0007390484,0.2140883,0.003742858,0.00003329427],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006050163,0.0002303178,0.9911368,0.0001846367,0.00006136818,0.0000398376,0.0000494239,0.0001479298,0.002099566],"genre_scores_gemma":[0.3949736,0.0004882252,0.5942101,0.0002277718,0.000228142,0.0004078561,0.0003915074,0.000288635,0.008784125],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006348979,"threshold_uncertainty_score":0.03352195,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02563025251408556,"score_gpt":0.2286485725729868,"score_spread":0.2030183200589012,"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."}}