{"id":"W4236454640","doi":"10.1002/9780470012505.tam015","title":"Maximum Likelihood","year":2004,"lang":"en","type":"other","venue":"Encyclopedia of Actuarial Science","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Maximum likelihood; Series (stratigraphy); Estimation; Indirect Inference; Econometrics; Computer science; Mathematics; Statistics; Applied mathematics; Artificial intelligence; Economics; Geology","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":[],"category_scores_codex":[0.0007461245,0.0002553336,0.000615837,0.0006183725,0.0001001834,0.00004606956,0.0008784293,0.0002911928,0.002151513],"category_scores_gemma":[0.0004451182,0.0002841265,0.0001445525,0.0006652432,0.0004674946,0.0002046423,0.0001688622,0.0002421775,0.0007345056],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001489473,"about_ca_system_score_gemma":0.0004785527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004699705,"about_ca_topic_score_gemma":0.0002007698,"domain_scores_codex":[0.9979374,0.000005506743,0.0006494242,0.0007295379,0.0001554832,0.0005226044],"domain_scores_gemma":[0.998551,0.00002248737,0.0006617781,0.000577685,0.0000368369,0.0001502791],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00008496835,0.0007571827,0.009515157,0.0004701066,0.0001049222,0.00002002115,0.004133387,0.0001077993,0.00009677307,0.7311616,0.1900172,0.06353091],"study_design_scores_gemma":[0.0004053275,0.0000764677,0.0009409451,0.0001104967,0.000007782379,7.837871e-7,0.00001385073,0.0001087801,0.00002818937,0.1267984,0.8710973,0.0004115985],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.0008841773,0.001590352,0.006565345,0.00008333034,0.003077301,0.0002955291,0.0001883041,0.00008569325,0.9872299],"genre_scores_gemma":[0.2618892,0.01807754,0.0576477,0.0003804185,0.008953484,0.00009680503,0.0000887256,0.001441276,0.6514248],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.6810802,"threshold_uncertainty_score":0.9999611,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01472873843419075,"score_gpt":0.2252184925882939,"score_spread":0.2104897541541031,"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."}}