{"id":"W2790909449","doi":"10.1002/wics.110","title":"Likelihood inference","year":2010,"lang":"en","type":"review","venue":"Wiley Interdisciplinary Reviews Computational Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Likelihood function; Likelihood principle; Inference; Empirical likelihood; Maximum likelihood; Restricted maximum likelihood; Marginal likelihood; Parametric statistics; Quasi-maximum likelihood; Computer science; Bayesian inference; Bayesian probability; Likelihood-ratio test; Econometrics; Mathematics; Artificial intelligence; Statistics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.009688513,0.001650708,0.002512597,0.004261473,0.0008083459,0.005007791,0.004577209,0.002828082,0.0345883],"category_scores_gemma":[0.0394025,0.0009937657,0.001865733,0.003586621,0.002403092,0.004605915,0.00321766,0.00351805,0.02386963],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001713539,"about_ca_system_score_gemma":0.002829124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001771035,"about_ca_topic_score_gemma":0.001287695,"domain_scores_codex":[0.9923714,0.004078253,0.000426519,0.00104876,0.00189896,0.0001761138],"domain_scores_gemma":[0.9839994,0.01073099,0.0007248698,0.002176613,0.002191369,0.0001766831],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0000450584,0.00004626972,0.000790677,0.001370725,0.0003092676,0.0001698301,0.0001298356,0.02210784,0.0003825882,0.4510351,0.046262,0.4773507],"study_design_scores_gemma":[0.00003635834,0.00002427834,0.0004375402,0.0008901265,0.00008124997,0.0004237213,0.0000679982,0.05068002,0.000866406,0.7352894,0.2111522,0.00005075966],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004675658,0.01179303,0.9558491,0.002225986,0.0004408098,0.0001503214,0.0008586047,0.0009381879,0.02727644],"genre_scores_gemma":[0.08043692,0.05324703,0.8042477,0.002971102,0.002938885,0.001086551,0.005690175,0.001390713,0.04799094],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0345883,"threshold_uncertainty_score":0.1157094,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1195520516965725,"score_gpt":0.4714508139783933,"score_spread":0.3518987622818208,"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."}}