{"id":"W4390608160","doi":"10.1002/cjs.11800","title":"Bayesian Model Selection via Composite Likelihood for High‐dimensional Data Integration","year":2024,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Marginal likelihood; Model selection; Selection (genetic algorithm); Bayesian information criterion; Bayesian probability; Gaussian; Quasi-maximum likelihood; Infinity; Mathematics; Generalized linear model; Maximum likelihood; Statistics; Computer science; Machine learning; Likelihood function","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0006872143,0.0001563002,0.0002795916,0.0002532952,0.0001532116,0.0001832131,0.0002725459,0.0000894418,0.000135805],"category_scores_gemma":[0.001214342,0.0001357146,0.00004115872,0.0001741866,0.00007229754,0.0002168076,0.00001607325,0.0003082542,0.000005259566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001843866,"about_ca_system_score_gemma":0.001542371,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004789981,"about_ca_topic_score_gemma":0.006839553,"domain_scores_codex":[0.9986439,0.00006968808,0.0005698327,0.0002019818,0.0002184977,0.0002961367],"domain_scores_gemma":[0.9975233,0.001180891,0.0001632405,0.0001908418,0.0004880676,0.0004536382],"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":[0.00002341842,0.00001900317,0.0000253488,0.0001482768,0.0000812478,0.00007268298,0.0001455646,0.00008406326,0.0006558392,0.7783962,0.06271294,0.1576353],"study_design_scores_gemma":[0.000108969,0.00009898943,0.00003382586,0.0001304437,0.0001157367,0.00008699464,0.000008274029,0.452538,0.0001539531,0.5463991,0.0002354854,0.00009026031],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001796941,0.0001134128,0.9940158,0.0004019335,0.000685821,0.000152703,0.004363855,0.00001441222,0.00007233168],"genre_scores_gemma":[0.157208,0.000005625946,0.8422362,0.00009544245,0.0002345195,0.000002883732,0.0001354908,0.000030437,0.0000513473],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4524539,"threshold_uncertainty_score":0.5534279,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06534678948256736,"score_gpt":0.3426255660040473,"score_spread":0.2772787765214799,"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."}}