{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002845246,0.0001118366,0.0002191332,0.0004677572,0.0001819422,0.00005937392,0.0006104728,0.0000588707,0.00003064046],"category_scores_gemma":[0.0001725253,0.00009764684,0.0000579938,0.0004960984,0.00008947073,0.000241599,0.00001568034,0.0002663479,0.00002041068],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001122923,"about_ca_system_score_gemma":0.002247093,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007219736,"about_ca_topic_score_gemma":0.001356535,"domain_scores_codex":[0.9989094,0.00008376644,0.0003228833,0.0001270388,0.000219214,0.0003377083],"domain_scores_gemma":[0.9980936,0.0001752583,0.0002116166,0.0002142688,0.0004080666,0.0008971985],"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":[0.000006222572,0.0000264204,0.0007119225,0.000007984464,0.00006355498,0.004865919,0.00206889,0.0000274071,0.00001249331,0.7132908,0.05402879,0.2248896],"study_design_scores_gemma":[0.001296304,0.001243251,0.01878828,0.0001057021,0.00006882559,0.006859147,0.00004833627,0.03522532,0.000446415,0.8921879,0.04294388,0.0007866307],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001042051,0.0006581981,0.9953371,0.0002055189,0.0006729526,0.00004347319,0.00004527852,0.000005602548,0.001989771],"genre_scores_gemma":[0.1759741,0.00006244983,0.8234028,0.0002864862,0.00012155,3.912672e-7,5.429192e-7,0.000007622531,0.0001440325],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.224103,"threshold_uncertainty_score":0.3986246,"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."}}