{"id":"W2140198391","doi":"10.1007/s10479-009-0532-5","title":"Approximating zero-variance importance sampling in a reliability setting","year":2009,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Estimator; Mathematical optimization; Interpolation (computer graphics); Importance sampling; Applied mathematics; Approximation error; Function (biology); Mathematics; Markov chain; Bounded function; Heuristics; Computer science; Variance (accounting); Reliability (semiconductor); Parameterized complexity; Monte Carlo method; Algorithm; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.03466161,0.0001030224,0.0003094544,0.0004401838,0.0004655768,0.0002957142,0.0008793233,0.00009996087,0.00008493676],"category_scores_gemma":[0.03190349,0.00008038527,0.00008809782,0.002271308,0.0002245742,0.0009037022,0.0001508425,0.0005472588,0.00004176431],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003850449,"about_ca_system_score_gemma":0.0003434852,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002216801,"about_ca_topic_score_gemma":0.0002827682,"domain_scores_codex":[0.9948292,0.0009058398,0.001327603,0.0006376958,0.001770007,0.0005296424],"domain_scores_gemma":[0.9948812,0.00182541,0.00008900755,0.001046401,0.002047147,0.0001108704],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001590848,0.001146332,0.05432845,0.00005156966,0.0000113683,0.00001200459,0.008563577,0.6401318,0.01301043,0.1282557,0.002721942,0.1516077],"study_design_scores_gemma":[0.0002582151,0.0001707384,0.0549227,0.0001074924,8.883221e-7,0.000003203434,0.000867568,0.3580744,0.003695996,0.5810833,0.0006379114,0.000177575],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9473664,0.0003100367,0.03135465,0.0165928,0.00003133202,0.0005284077,0.00001575487,0.00001966392,0.003781002],"genre_scores_gemma":[0.9616565,0.00005641781,0.03769691,0.0002338718,0.00003366041,0.00002748518,0.000003716463,0.000004764216,0.0002867435],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4528276,"threshold_uncertainty_score":0.994019,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5793130532141241,"score_gpt":0.5796847489334721,"score_spread":0.0003716957193480308,"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."}}