{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03707017,0.002000065,0.004232003,0.003348256,0.001500102,0.003664619,0.00565171,0.002996931,0.002469652],"category_scores_gemma":[0.0998841,0.002506306,0.00353507,0.004394877,0.004644028,0.004393106,0.007254621,0.005909085,0.0006533113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002221506,"about_ca_system_score_gemma":0.003493401,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005363182,"about_ca_topic_score_gemma":0.004039929,"domain_scores_codex":[0.9805133,0.01397528,0.0006606372,0.002056337,0.002286647,0.0005078207],"domain_scores_gemma":[0.8836004,0.1029602,0.004576426,0.003829967,0.003579713,0.001453195],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004144227,0.0002157454,0.005485202,0.0002966554,0.0006198752,0.0005245465,0.0004296797,0.8021679,0.001388929,0.130864,0.001615168,0.05597793],"study_design_scores_gemma":[0.00002511183,0.0000286421,0.0002783398,0.00001538635,0.00002284654,0.00002852815,0.00001162781,0.9609402,0.0002080547,0.03817998,0.0002428704,0.00001856759],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004959889,0.0001089659,0.9944052,0.0001907047,0.00001144542,0.00004060443,0.00003661569,0.00009548463,0.0001511192],"genre_scores_gemma":[0.3043943,0.0006138406,0.6894462,0.0004862769,0.0003120586,0.0009695679,0.001045984,0.0003067368,0.002424981],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.03707017,"threshold_uncertainty_score":0.1960481,"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."}}