{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01790995,0.001447567,0.002153985,0.002551512,0.001246338,0.002288784,0.003363579,0.002145524,0.005173127],"category_scores_gemma":[0.04946827,0.001458116,0.002153499,0.002037991,0.001941663,0.004272839,0.00394858,0.005281725,0.001207565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002327614,"about_ca_system_score_gemma":0.00188575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005053574,"about_ca_topic_score_gemma":0.007016026,"domain_scores_codex":[0.9920722,0.005737649,0.0002441203,0.0009212214,0.0007357885,0.000289039],"domain_scores_gemma":[0.9644797,0.03079254,0.0009394391,0.001768806,0.001552816,0.0004665938],"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":[0.0002491229,0.0001780082,0.004725802,0.0002326681,0.0003310964,0.0002423389,0.0005398381,0.3571534,0.0008729903,0.519187,0.008923751,0.1073639],"study_design_scores_gemma":[0.00001777103,0.00001956719,0.0001810217,0.00002555321,0.00001325424,0.00003490025,0.0000277854,0.8426688,0.0001728744,0.1556269,0.001197492,0.0000140725],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006905072,0.0003073026,0.9912412,0.0004590357,0.00002976949,0.00004963771,0.0001423437,0.0001692996,0.0006962512],"genre_scores_gemma":[0.3720233,0.001334977,0.6125494,0.001065889,0.0003740331,0.001037395,0.002376894,0.0004962598,0.008741732],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01790995,"threshold_uncertainty_score":0.09471804,"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."}}