{"id":"W2911264190","doi":"10.1109/wsc.2018.8632237","title":"EXACT POSTERIOR SIMULATION FROM THE LINEAR LASSO REGRESSION","year":2018,"lang":"en","type":"article","venue":"2018 Winter Simulation Conference (WSC)","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Lasso (programming language); Computer science; Linear regression; Artificial intelligence; Statistics; Mathematics; Machine learning; World Wide Web","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":[0.006068648,0.0007992153,0.001559923,0.0008105445,0.000747324,0.00184719,0.001756118,0.001619017,0.007515374],"category_scores_gemma":[0.02956735,0.0007500963,0.001097166,0.001007396,0.001804575,0.002260185,0.002320583,0.00275709,0.001076441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001188586,"about_ca_system_score_gemma":0.002161058,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005583495,"about_ca_topic_score_gemma":0.004611294,"domain_scores_codex":[0.9969728,0.001751383,0.0001057854,0.0004259018,0.0005284502,0.0002156899],"domain_scores_gemma":[0.986133,0.01111423,0.0005595784,0.001171543,0.0007767869,0.000244858],"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":[0.0001410065,0.00004030491,0.00196703,0.00008326185,0.00003794357,0.00008612816,0.0001123321,0.8577195,0.000423559,0.1185143,0.001626768,0.01924786],"study_design_scores_gemma":[0.0000176035,0.00001094632,0.0001235204,0.00001602101,0.000004496464,0.00001401932,0.000009781706,0.9645688,0.0002083701,0.03445668,0.0005612049,0.000008633133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0154109,0.0001661165,0.9804066,0.0003367717,0.00004415019,0.00006603377,0.000206875,0.0003683728,0.00299414],"genre_scores_gemma":[0.5951564,0.0005425892,0.394551,0.0004495263,0.0001513486,0.0007968327,0.001738832,0.0004053742,0.006207988],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007515374,"threshold_uncertainty_score":0.03209448,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1559523577793533,"score_gpt":0.4135504450014543,"score_spread":0.2575980872221011,"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."}}