{"id":"W3198205467","doi":"10.82308/13578","title":"Bayesian model selection for deep exponential families","year":2016,"lang":"en","type":"article","venue":"eScholarship@McGill (McGill)","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"McGill University","keywords":"Model selection; Bayesian probability; Selection (genetic algorithm); Econometrics; Computer science; Artificial intelligence; Statistics; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001184348,0.0004663177,0.0004380097,0.0003041576,0.001003388,0.0001270081,0.001174355,0.0003332336,0.0000281412],"category_scores_gemma":[0.0003282328,0.0003685248,0.0003367317,0.0004868493,0.00007452609,0.002078258,0.0003376309,0.0003133233,0.00006177415],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003191379,"about_ca_system_score_gemma":0.00004984728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003220277,"about_ca_topic_score_gemma":0.000118308,"domain_scores_codex":[0.9965345,0.000305072,0.0005738999,0.001192141,0.0004937925,0.000900629],"domain_scores_gemma":[0.9979464,0.0003022761,0.000222015,0.000852528,0.0003107374,0.0003660532],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00003142815,0.00005475167,0.000004815496,0.00001637,0.00002836848,0.000002229547,0.000004177871,0.00006964433,0.09827944,0.435322,0.000008528987,0.4661782],"study_design_scores_gemma":[0.001484236,0.000212926,0.00003187594,0.00006957903,0.0000442762,0.00004490124,0.000005550954,0.1245637,0.1859493,0.6731965,0.01360156,0.0007956401],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02588504,0.00006198043,0.9639601,0.0001922719,0.0006801249,0.0005653383,0.0001209915,0.00054307,0.00799114],"genre_scores_gemma":[0.5709381,0.00005392187,0.4273244,0.0003849628,0.00005774835,0.0001403074,0.000003172608,0.00005324366,0.001044172],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.545053,"threshold_uncertainty_score":0.9998767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01923236448092903,"score_gpt":0.2448401688532345,"score_spread":0.2256078043723055,"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."}}