{"id":"W1914588449","doi":"10.18637/jss.v019.i09","title":"<b>tgp</b>: An<i>R</i>Package for Bayesian Nonstationary, Semiparametric Nonlinear Regression and Design by Treed Gaussian Process Models","year":2007,"lang":"en","type":"article","venue":"Journal of Statistical Software","topic":"Gaussian Processes and Bayesian Inference","field":"Computer Science","cited_by":206,"is_retracted":false,"has_abstract":true,"ca_institutions":"Booth University College","funders":"","keywords":"Bayesian probability; Gaussian process; Semiparametric regression; Gaussian; Computer science; Nonlinear system; Bayesian inference; Inference; Dimension (graph theory); Mathematics; Applied mathematics; Algorithm; Regression; Artificial intelligence; Statistics","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":[0.005253334,0.002197311,0.001655169,0.002024638,0.0006164,0.001698517,0.002637887,0.001627506,0.1729673],"category_scores_gemma":[0.02855063,0.001842141,0.002057958,0.002152727,0.000650985,0.002261326,0.001969556,0.003312712,0.09374729],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005454118,"about_ca_system_score_gemma":0.001993751,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003028661,"about_ca_topic_score_gemma":0.00364883,"domain_scores_codex":[0.9980254,0.0009443271,0.0001868213,0.0002599888,0.0004752375,0.0001083183],"domain_scores_gemma":[0.9848796,0.0104541,0.0009007039,0.001957347,0.001565066,0.0002432229],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002905438,0.0001289261,0.001316794,0.001615718,0.0004325233,0.000279941,0.0002299215,0.01625939,0.00517019,0.05550022,0.6510856,0.2676903],"study_design_scores_gemma":[0.0005115939,0.0001974304,0.003355549,0.0004263212,0.0002396084,0.001046215,0.00005631512,0.1863865,0.01450629,0.1516405,0.6413303,0.0003033325],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.0005219552,0.0001414901,0.9360856,0.0002697393,0.0001498446,0.0001693277,0.01589873,0.04351592,0.003247375],"genre_scores_gemma":[0.007965322,0.0003075983,0.9325506,0.000408112,0.0001118154,0.002061805,0.01682564,0.03166662,0.008102498],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.1729673,"threshold_uncertainty_score":0.5786332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0226674164356033,"score_gpt":0.3016473060079822,"score_spread":0.2789798895723788,"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."}}