{"id":"W2002447070","doi":"10.4236/ojs.2015.51007","title":"Combining Likelihood Information from Independent Investigations","year":2015,"lang":"en","type":"article","venue":"Open Journal of Statistics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Likelihood function; Mathematics; Fisher information; Likelihood principle; Statistics; Maximum likelihood; Marginal likelihood; Scoring algorithm; Score test; Restricted maximum likelihood; Estimation theory; Likelihood-ratio test; Maximum likelihood sequence estimation; Expectation–maximization algorithm; Applied mathematics; Quasi-maximum likelihood","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":[],"consensus_categories":[],"category_scores_codex":[0.0006370405,0.0001078582,0.0002456281,0.00007838419,0.0001099183,0.0003035713,0.0004272067,0.00005406629,0.0002878883],"category_scores_gemma":[0.003985049,0.0000987419,0.0000284753,0.0001902154,0.00007133133,0.0007957154,0.0001064851,0.0002230229,0.0001420891],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001144853,"about_ca_system_score_gemma":0.0004524968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005575342,"about_ca_topic_score_gemma":0.0000143097,"domain_scores_codex":[0.998346,0.00008165537,0.0008695088,0.00007034401,0.0004925137,0.0001399358],"domain_scores_gemma":[0.9969156,0.0006465843,0.0007881497,0.0001728847,0.001101418,0.000375388],"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":[0.00002193383,0.000105906,0.0002888441,0.000008316313,0.00003829777,0.000005182077,0.0007975933,0.00003474198,0.00001874995,0.8768735,0.1150512,0.006755732],"study_design_scores_gemma":[0.001452223,0.000103667,0.002508645,0.00005746533,0.00008695971,0.00002643248,0.001088743,0.003517685,0.0001305815,0.9782634,0.01263274,0.0001314021],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003744462,0.000010333,0.988804,0.0007290514,0.000203337,0.0002048499,0.001589039,0.00001427867,0.004700647],"genre_scores_gemma":[0.3060134,0.000006326084,0.6931547,0.0003425478,0.00005910358,0.00001182896,0.0003533476,0.00001086266,0.00004787128],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.302269,"threshold_uncertainty_score":0.4770764,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1376655204114027,"score_gpt":0.3801974429491913,"score_spread":0.2425319225377885,"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."}}