{"id":"W2018703907","doi":"10.1080/15598608.2008.10411880","title":"A Note on Monte Carlo Maximization by the Density Ratio Model","year":2008,"lang":"en","type":"article","venue":"Journal of Statistical Theory and Practice","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"University of Lethbridge","keywords":"Mathematics; Monte Carlo method; Estimator; Applied mathematics; Statistical inference; Inference; Empirical likelihood; Maximization; Restricted maximum likelihood; Expectation–maximization algorithm; Density estimation; Statistics; Estimation theory; Maximum likelihood; Mathematical optimization; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.03735507,0.002296851,0.004452045,0.002428416,0.00149828,0.004486483,0.006554949,0.005411407,0.005821047],"category_scores_gemma":[0.1299602,0.002726209,0.004222596,0.003236943,0.009285336,0.01298011,0.008431383,0.01364092,0.001724934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002899807,"about_ca_system_score_gemma":0.003107788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005577793,"about_ca_topic_score_gemma":0.003265302,"domain_scores_codex":[0.9766651,0.01746359,0.000756025,0.001805661,0.002922452,0.0003871355],"domain_scores_gemma":[0.897156,0.09260762,0.001195832,0.006054414,0.002275758,0.0007104072],"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":[0.00004775069,0.00001978003,0.0002502826,0.0001126445,0.0000905672,0.0000742406,0.0001115816,0.02525159,0.0001985319,0.9510264,0.004900773,0.01791587],"study_design_scores_gemma":[0.00001677055,0.0000136147,0.0000801054,0.00004893556,0.00002731397,0.00006729783,0.000008301662,0.1000267,0.0001920324,0.8939593,0.005526433,0.00003315744],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004484958,0.001110985,0.9930052,0.002427348,0.0002399348,0.00002105194,0.00004435981,0.0001091049,0.002593548],"genre_scores_gemma":[0.06174869,0.004196441,0.917006,0.00445467,0.003202124,0.0004594069,0.0002135093,0.001144241,0.007574983],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03735507,"threshold_uncertainty_score":0.1975548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09924697584145112,"score_gpt":0.401348454410224,"score_spread":0.3021014785687729,"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."}}