{"id":"W2793458543","doi":"10.1002/cjs.11570","title":"Weighted Bayesian bootstrap for scalable posterior distributions","year":2020,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Scalability; Posterior probability; Sampling (signal processing); Uncertainty quantification; Bayesian inference; Statistical learning; Bayesian statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009624854,0.0001125765,0.0001955388,0.0000881193,0.0002015865,0.0003103102,0.0006773148,0.00004839989,0.00008034077],"category_scores_gemma":[0.0002001602,0.0001051538,0.00005600841,0.0002942513,0.00006732246,0.0003009052,0.00001757975,0.0001520288,0.0000107752],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007383432,"about_ca_system_score_gemma":0.002252026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001564629,"about_ca_topic_score_gemma":0.0008502436,"domain_scores_codex":[0.9989914,0.00001777434,0.0003728361,0.0001451915,0.0001328945,0.0003398975],"domain_scores_gemma":[0.9980454,0.00007412675,0.0002136892,0.0001347015,0.0004450767,0.00108699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002548635,0.00003043507,0.002270907,0.0002267783,0.00009163941,0.0006850353,0.001390555,0.00003102265,0.0002811142,0.7712667,0.1257913,0.097909],"study_design_scores_gemma":[0.003716282,0.003873547,0.01690036,0.0004512572,0.0002520522,0.001504176,0.0003728217,0.1907827,0.003440006,0.3800364,0.3969377,0.001732696],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0003556623,0.0001355222,0.989625,0.008297432,0.0002676642,0.00009135075,0.001004995,0.00001001686,0.0002123947],"genre_scores_gemma":[0.6252163,0.000007408983,0.3737725,0.0007993112,0.0001357562,0.00000209531,0.00001541505,0.00000905113,0.00004214003],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6248606,"threshold_uncertainty_score":0.4288047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02668371465638351,"score_gpt":0.2396629175035957,"score_spread":0.2129792028472122,"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."}}