{"id":"W7017419279","doi":"","title":"Bayesian Methods for Data Integration and High Dimensional Linear Model with Non-Sparsity","year":2025,"lang":"en","type":"other","venue":"York University Digital Library (York University)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Markov chain Monte Carlo; Model selection; Bayesian probability; Consistency (knowledge bases); Information Criteria; Data integration; Bayesian inference; Marginal likelihood; Bayesian linear regression; Importance sampling","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":[0.01759037,0.002445617,0.002836485,0.003477226,0.001340506,0.002905082,0.004448832,0.002453959,0.004940218],"category_scores_gemma":[0.05436885,0.00256327,0.002881872,0.004205381,0.002865767,0.003548108,0.005472863,0.006444029,0.001611799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002339151,"about_ca_system_score_gemma":0.004333853,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009789209,"about_ca_topic_score_gemma":0.01011529,"domain_scores_codex":[0.9905869,0.006368068,0.0003720229,0.001185768,0.00126999,0.0002172587],"domain_scores_gemma":[0.9653739,0.02791466,0.001886903,0.002640623,0.001741704,0.0004421605],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001594307,0.0001558761,0.004293693,0.0005350423,0.0006223105,0.0002805574,0.0004542913,0.4810365,0.001171851,0.3347674,0.006339551,0.1701835],"study_design_scores_gemma":[0.00003155743,0.00002484945,0.0003166725,0.00006000657,0.00003484764,0.00003657467,0.0000228295,0.8252203,0.000309092,0.1707904,0.003123848,0.00002897613],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005210864,0.0001456892,0.9986829,0.0001584411,0.00001603083,0.00003304474,0.00005933103,0.000159441,0.0002240817],"genre_scores_gemma":[0.05820921,0.0009363081,0.9359365,0.0003967449,0.000254384,0.001020174,0.0009134801,0.0003535752,0.001979614],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01759037,"threshold_uncertainty_score":0.09302783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02026778949549404,"score_gpt":0.2317154393385732,"score_spread":0.2114476498430792,"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."}}