{"id":"W4255223167","doi":"10.1002/9781118014967.ch13","title":"Cross‐Entropy Method","year":2011,"lang":"en","type":"other","venue":"Wiley series in probability and statistics","topic":"Simulation Techniques and Applications","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Cross-entropy method; Monte Carlo method; Mathematical optimization; Adaptive sampling; Computer science; Closeness; Importance sampling; Cross entropy; Entropy (arrow of time); Divergence (linguistics); Algorithm; Sampling (signal processing); Kullback–Leibler divergence; Continuous optimization; Principle of maximum entropy; Optimization problem; Mathematics; Artificial intelligence; Statistics; Multi-swarm optimization","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001399322,0.0002116336,0.0004193494,0.0001934045,0.00008148927,0.0001718348,0.0004282999,0.0002747217,0.009858482],"category_scores_gemma":[0.00169253,0.0001727902,0.00004045521,0.0003271241,0.0004825349,0.00009292216,0.0001746155,0.0002123465,0.00008931409],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002913541,"about_ca_system_score_gemma":0.00006572736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003247843,"about_ca_topic_score_gemma":0.0007797498,"domain_scores_codex":[0.9978356,0.0002197284,0.0006775991,0.0006347171,0.0004311764,0.0002011958],"domain_scores_gemma":[0.9979409,0.0006908688,0.0003082721,0.0008232461,0.0001510161,0.00008568305],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002945615,0.00007687017,0.008306885,0.00005257554,0.000009070111,0.00000315323,0.0002497875,0.00001158247,0.000001342257,0.6191764,0.2631441,0.1089387],"study_design_scores_gemma":[0.00007628528,0.00002840379,0.001423277,0.0000271629,0.000004903533,0.000002129971,0.00001767106,0.0002206734,0.000009695725,0.5182898,0.4797766,0.0001233989],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00003081123,0.0003997411,0.7992687,0.00009932362,0.0002098545,0.0007907487,0.001992289,0.00016276,0.1970457],"genre_scores_gemma":[0.0001740479,0.0003707613,0.6777102,0.0000690816,0.00005459553,0.00008795584,0.00004918203,0.00007412447,0.3214101],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2166325,"threshold_uncertainty_score":0.9910467,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1011182166886213,"score_gpt":0.4236077886715368,"score_spread":0.3224895719829154,"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."}}