{"id":"W7133084412","doi":"","title":"Advances in Scalable Bayesian Inference: Gaussian Processes & Discrete Variable Models","year":2022,"lang":"","type":"dissertation","venue":"TSpace","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Kenneth M. Molson Foundation; University of Toronto; Natural Sciences and Engineering Research Council of Canada; Molson Foundation","keywords":"Inference; Gaussian process; Approximate inference; Kernel (algebra); Bayesian inference; Bayesian probability; Fiducial inference; Computational complexity theory","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00568426,0.001316599,0.001880804,0.001152613,0.0008587067,0.003438181,0.003795169,0.002114382,0.00388522],"category_scores_gemma":[0.0255862,0.001324005,0.001900432,0.002357541,0.002342871,0.005256807,0.003522575,0.006424385,0.001378807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002325881,"about_ca_system_score_gemma":0.00290174,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01187685,"about_ca_topic_score_gemma":0.009481355,"domain_scores_codex":[0.9972535,0.001208904,0.0001201303,0.00049198,0.0007868629,0.0001386296],"domain_scores_gemma":[0.9893724,0.007820483,0.0004840542,0.001282994,0.0008678593,0.0001721912],"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":[0.00004029882,0.00005463314,0.0007791315,0.000174849,0.00009989923,0.00008068889,0.0001376141,0.4088581,0.0006522298,0.5074897,0.006352223,0.07528061],"study_design_scores_gemma":[0.000006919702,0.000004578375,0.00007788192,0.00002015217,0.00000595697,0.00001186257,0.000009618157,0.8353274,0.0001466557,0.1621107,0.002269835,0.000008427379],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00112169,0.0005475653,0.995666,0.0009331162,0.00006327509,0.00001393041,0.00006875432,0.000206187,0.001379639],"genre_scores_gemma":[0.1483987,0.004083042,0.8400469,0.0008413729,0.0008347651,0.0002649817,0.0006207431,0.0005066029,0.004402773],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01187685,"threshold_uncertainty_score":0.0300616,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01957041236336576,"score_gpt":0.3319841851377113,"score_spread":0.3124137727743456,"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."}}