{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01236512,0.001297717,0.001837931,0.00289929,0.0008976075,0.002159947,0.003228371,0.002047614,0.006093653],"category_scores_gemma":[0.06767501,0.001173931,0.001512755,0.002516801,0.002425931,0.003438999,0.003870367,0.004297104,0.001789593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001588596,"about_ca_system_score_gemma":0.002033583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004510238,"about_ca_topic_score_gemma":0.003996572,"domain_scores_codex":[0.9939017,0.003122156,0.000244292,0.0006743831,0.001767557,0.0002898918],"domain_scores_gemma":[0.9742334,0.0183682,0.001056686,0.003073225,0.002750653,0.000517832],"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":[0.0001960993,0.0001218835,0.001752652,0.0002900957,0.0001856804,0.0002873993,0.0001627959,0.3829534,0.003437849,0.5042196,0.006686978,0.09970559],"study_design_scores_gemma":[0.00001917723,0.00001543676,0.0002012745,0.00004187772,0.000008985948,0.00003348701,0.00001208859,0.8335708,0.0006301224,0.1635049,0.001948214,0.00001368599],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001978446,0.0001180924,0.9967306,0.0001219533,0.00002801023,0.00003408883,0.00008828083,0.0002762706,0.0006243774],"genre_scores_gemma":[0.2486466,0.000784396,0.7441579,0.0004256712,0.0003137499,0.0008342406,0.00134653,0.0008002021,0.002690753],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01236512,"threshold_uncertainty_score":0.06539375,"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."}}