{"id":"W4297347715","doi":"10.3390/app12199668","title":"Projection Pursuit Multivariate Sampling of Parameter Uncertainty","year":2022,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Latin hypercube sampling; Projection pursuit; Monte Carlo method; Sampling (signal processing); Multivariate statistics; Rejection sampling; Mathematics; Projection (relational algebra); Statistics; Slice sampling; Algorithm; Computer science; Importance sampling; Markov chain Monte Carlo; Hybrid Monte Carlo","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006091211,0.0001057367,0.0002178422,0.0002769416,0.0006131512,0.000111687,0.001140859,0.00002843746,0.0003422228],"category_scores_gemma":[0.001124316,0.00007449809,0.00006671908,0.001745067,0.0003269504,0.000106052,0.000301169,0.0001497081,0.00002196473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005073558,"about_ca_system_score_gemma":0.0001451757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008592773,"about_ca_topic_score_gemma":0.000003357169,"domain_scores_codex":[0.9969699,0.0001020417,0.0004601218,0.0005075124,0.001695846,0.0002645307],"domain_scores_gemma":[0.9976868,0.001657558,0.0002239762,0.0003070659,0.00007278108,0.00005181755],"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":[0.00005293703,0.0001074876,0.0005949328,0.00000664476,0.00001314029,0.000001099366,0.001052364,0.8285372,0.02440035,0.1039652,0.001350449,0.03991821],"study_design_scores_gemma":[0.0009603626,0.0006591201,0.007098696,0.00001523162,0.00003636243,0.00002793578,0.008695283,0.6476853,0.003923079,0.2951689,0.03495846,0.0007712779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2875657,0.00009011737,0.6962934,0.0003824159,0.001263308,0.0007280878,0.00002860095,0.000135561,0.01351276],"genre_scores_gemma":[0.96652,7.254421e-7,0.03305517,0.00006892077,0.000031228,0.00008195914,0.000001168172,0.000004838457,0.0002360547],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6789542,"threshold_uncertainty_score":0.4715925,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2053794927832376,"score_gpt":0.375858765116633,"score_spread":0.1704792723333953,"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."}}