{"id":"W1959135937","doi":"10.1109/aps.2015.7305277","title":"Uncertainty quantification of ray-tracing based wireless propagation models with a Control Variate-Polynomial Chaos Expansion method","year":2015,"lang":"en","type":"article","venue":"","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Polynomial chaos; Control variates; Monte Carlo method; Random variate; Convergence (economics); Ray tracing (physics); Polynomial; Applied mathematics; Uncertainty quantification; Algorithm; Computer science; Tracing; Polynomial expansion; Taylor series; Mathematical optimization; Mathematics; Hybrid Monte Carlo; Statistics; Random variable; Mathematical analysis; Physics; Machine learning; Optics","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":[0.001330075,0.0006427401,0.0006218893,0.0008545617,0.0003305029,0.0007236083,0.0008376022,0.0006887623,0.0005334027],"category_scores_gemma":[0.003566879,0.0003243137,0.0007218627,0.0006166817,0.0007113958,0.0008332831,0.0008984507,0.0008461802,0.00008990701],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007556273,"about_ca_system_score_gemma":0.0008995887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004105714,"about_ca_topic_score_gemma":0.001693024,"domain_scores_codex":[0.9992914,0.0002736391,0.00002913561,0.00007128262,0.0002913974,0.00004301474],"domain_scores_gemma":[0.9982589,0.001208636,0.0001857261,0.00008686962,0.0002294538,0.00003041107],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001763064,0.000007431529,0.0001625157,0.0000193359,0.00000945953,0.00001747357,0.00002156677,0.9827079,0.001547915,0.008926978,0.00006683409,0.006495011],"study_design_scores_gemma":[4.847747e-7,0.000003411052,0.00002078952,8.887965e-7,0.000001129436,0.00000338879,8.248718e-7,0.9990466,0.0002430672,0.0006368292,0.00004077244,0.00000180438],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007887213,0.00005086038,0.9915309,0.00002639163,0.000003594045,0.00001245172,0.00001289021,0.00005789264,0.0004177791],"genre_scores_gemma":[0.8370588,0.0003424698,0.1605665,0.00003606904,0.00002636752,0.0001455888,0.00008392824,0.00007054133,0.001669826],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004105714,"threshold_uncertainty_score":0.008163631,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1241462462186121,"score_gpt":0.3354912375193543,"score_spread":0.2113449913007423,"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."}}