{"id":"W2146029760","doi":"10.1109/ccece.1996.548125","title":"Variance reduction techniques for use with sequential Monte Carlo simulation in bulk power system reliability evaluation","year":2002,"lang":"en","type":"article","venue":"","topic":"Power System Reliability and Maintenance","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Control variates; Variance reduction; Monte Carlo method; Reliability (semiconductor); Computer science; Reduction (mathematics); Variance (accounting); Reliability engineering; Process (computing); Sequential estimation; Power (physics); Algorithm; Statistics; Hybrid Monte Carlo; Mathematics; Engineering; Markov chain 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005331123,0.000802241,0.0008461967,0.001528405,0.000448986,0.0006595313,0.0007570466,0.0005793753,0.002839461],"category_scores_gemma":[0.02401563,0.0005862955,0.0009676859,0.00141849,0.0005521657,0.0007566747,0.0008915038,0.001552896,0.0005280577],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004325997,"about_ca_system_score_gemma":0.00118119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002768117,"about_ca_topic_score_gemma":0.003035801,"domain_scores_codex":[0.9951615,0.003456465,0.0001627983,0.0001473352,0.0009724149,0.00009953567],"domain_scores_gemma":[0.9862374,0.0110199,0.0004990679,0.001034066,0.001136869,0.00007262936],"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":[0.000198724,0.0001613787,0.001723284,0.0001147959,0.0001550159,0.0001149442,0.0001917829,0.6822128,0.004568571,0.06063393,0.001500362,0.2484244],"study_design_scores_gemma":[0.00002872626,0.00009663175,0.0003414675,0.00001116314,0.00001837832,0.00004948442,0.00001222785,0.982137,0.002067502,0.01410298,0.001114838,0.00001966448],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00242341,0.00002116412,0.9970353,0.00001595927,0.000006423002,0.00002464505,0.000008054083,0.0002333895,0.0002317181],"genre_scores_gemma":[0.1432957,0.0001270207,0.8551028,0.0000344088,0.00002472744,0.0004485719,0.00009317286,0.0002632294,0.0006103958],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005331123,"threshold_uncertainty_score":0.02819407,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02502391010234832,"score_gpt":0.2474750964614972,"score_spread":0.2224511863591489,"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."}}