{"id":"W4254001485","doi":"10.1002/9781118445112.stat04464","title":"Maximum Likelihood","year":2014,"lang":"en","type":"other","venue":"Wiley StatsRef: Statistics Reference Online","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Maximum likelihood; Series (stratigraphy); Indirect Inference; Estimation; Computer science; Mathematics; Econometrics; Statistics; Applied mathematics; Algorithm; Artificial intelligence; Economics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0004012435,0.0006754051,0.001361125,0.0005935761,0.0001168186,0.0001111795,0.0006454555,0.0006755882,0.006337692],"category_scores_gemma":[0.0003724085,0.0007806782,0.0001259453,0.0002642694,0.0001613831,0.00007234601,0.0001732758,0.0008026846,0.00427696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001441521,"about_ca_system_score_gemma":0.0001429164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002306025,"about_ca_topic_score_gemma":0.002679961,"domain_scores_codex":[0.9964996,0.00003997672,0.001299291,0.001146057,0.0001600383,0.0008550158],"domain_scores_gemma":[0.9973401,0.0001059823,0.001122586,0.001043047,0.000103031,0.0002852993],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002570293,0.0002990823,0.001221655,0.0003425163,0.0001127038,0.00001807737,0.00008897798,0.00001349171,0.000001581614,0.2929955,0.667684,0.03719677],"study_design_scores_gemma":[0.0004565189,0.000127754,0.0002937156,0.0002470492,0.00002485457,0.000001645567,0.00001291419,0.006203543,6.040474e-7,0.2316855,0.7602143,0.0007315928],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00006450818,0.006638674,0.6478201,0.0001160793,0.001237979,0.0004687442,0.08535439,0.0003448232,0.2579547],"genre_scores_gemma":[0.004394249,0.023362,0.4693413,0.0008996554,0.002345187,0.00009419285,0.01947552,0.002301668,0.4777862],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.2198315,"threshold_uncertainty_score":0.9994644,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04542689328164497,"score_gpt":0.2643005992282645,"score_spread":0.2188737059466195,"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."}}