{"id":"W2053652664","doi":"10.1080/10618600.2013.841584","title":"Parallel Bayesian Additive Regression Trees","year":2014,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":64,"is_retracted":false,"has_abstract":true,"ca_institutions":"Acadia University","funders":"Office of Science; U.S. Department of Energy","keywords":"Markov chain Monte Carlo; Computer science; Bayesian probability; Boosting (machine learning); Approximate Bayesian computation; Bayesian inference; Inference; Computation; Machine learning; Algorithm; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.003400634,0.001192323,0.001984149,0.001397199,0.001038347,0.002481682,0.003003026,0.001315965,0.0110483],"category_scores_gemma":[0.01349384,0.001017583,0.00183203,0.002866071,0.0006380563,0.002269974,0.002468961,0.002099894,0.006406764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001114229,"about_ca_system_score_gemma":0.002949681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009043022,"about_ca_topic_score_gemma":0.01351255,"domain_scores_codex":[0.9968283,0.001016142,0.0001509499,0.0006168818,0.001120106,0.0002676391],"domain_scores_gemma":[0.9965233,0.001348999,0.0002146234,0.0007611404,0.000998468,0.0001535113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003156425,0.0001546926,0.002427246,0.0002312598,0.0002863148,0.0001914314,0.000164112,0.490019,0.002132286,0.101305,0.02981983,0.3729533],"study_design_scores_gemma":[0.00004043473,0.00001874438,0.0003413324,0.00002010777,0.00003351994,0.00006992608,0.00001911775,0.9099917,0.0008722116,0.07629538,0.01227695,0.00002057482],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004104329,0.0003792522,0.9852609,0.0002798392,0.0001289519,0.00006228819,0.0008617754,0.003819112,0.005103602],"genre_scores_gemma":[0.1493558,0.0007477182,0.8288806,0.0004008455,0.0003629202,0.0003923985,0.004627334,0.001835281,0.01339718],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0110483,"threshold_uncertainty_score":0.0369603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007996393648830565,"score_gpt":0.2454976015265561,"score_spread":0.2375012078777256,"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."}}