{"id":"W4225900855","doi":"10.1109/tmag.2022.3159760","title":"Non-Parametric Belief Propagation Solver for Stochastic Systems of Linear Equations","year":2022,"lang":"en","type":"article","venue":"IEEE Transactions on Magnetics","topic":"Probabilistic and Robust Engineering Design","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Probabilistic logic; Solver; Belief propagation; Parametric statistics; Mathematical optimization; Partial differential equation; Stochastic partial differential equation; Probabilistic analysis of algorithms; Finite element method; Applied mathematics; Monte Carlo method; Algorithm; Mathematics; 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":[0.001815531,0.0008237879,0.001417299,0.0005230114,0.0005622127,0.001453807,0.001344539,0.001668839,0.004650477],"category_scores_gemma":[0.006044991,0.0006969234,0.0009169953,0.0009293597,0.0008789523,0.0009060799,0.001627231,0.003010816,0.0008299743],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009472106,"about_ca_system_score_gemma":0.002429414,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00848007,"about_ca_topic_score_gemma":0.008607922,"domain_scores_codex":[0.9993511,0.0002477969,0.0000345543,0.00008153198,0.0002276833,0.00005734407],"domain_scores_gemma":[0.996409,0.002910261,0.0001664276,0.00007534641,0.0003663258,0.00007263651],"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":[0.00003620946,0.00002235018,0.0002659787,0.0001042278,0.00003703641,0.00007252649,0.00005720856,0.9465313,0.0005039274,0.02909985,0.001208949,0.02206044],"study_design_scores_gemma":[0.000004310169,0.000003040088,0.00001325052,0.000004082483,0.000001996101,0.000004343365,0.000002942864,0.9959008,0.00008437658,0.003611388,0.0003677299,0.000001790304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001401551,0.0001306524,0.9964733,0.0001673732,0.0000286333,0.00002570038,0.00004309672,0.0001322972,0.001597433],"genre_scores_gemma":[0.2810531,0.0008045906,0.7027674,0.0003608015,0.000174499,0.0007585148,0.0005148275,0.0002772858,0.01328891],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00848007,"threshold_uncertainty_score":0.01686144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08036451097807835,"score_gpt":0.3139195243902346,"score_spread":0.2335550134121562,"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."}}