{"id":"W2770616710","doi":"10.1016/b978-0-444-53859-8.00003-5","title":"The Cross-Entropy Method for Optimization","year":2013,"lang":"en","type":"book-chapter","venue":"Handbook of statistics","topic":"Bayesian Modeling and Causal Inference","field":"Computer Science","cited_by":176,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"","keywords":"Cross-entropy method; Heuristics; Cross entropy; Mathematical optimization; Entropy (arrow of time); Minification; Kullback–Leibler divergence; Computer science; Optimization problem; Mathematics; Algorithm; Principle of maximum entropy; Artificial intelligence; Quadratic assignment problem","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":[0.0002825478,0.0002124704,0.0002695065,0.00004479017,0.0002150861,0.0002841498,0.000698448,0.000169619,0.00006824364],"category_scores_gemma":[0.00008721028,0.0001602346,0.00007702554,0.00001678843,0.0001334891,0.00009621747,0.0001202884,0.0001614771,0.00003538102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002936595,"about_ca_system_score_gemma":0.0001534221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007848727,"about_ca_topic_score_gemma":0.000002190024,"domain_scores_codex":[0.9987559,0.00001957066,0.0004402381,0.0003039749,0.0002693206,0.0002110413],"domain_scores_gemma":[0.9976033,0.000737329,0.0003966464,0.0005169276,0.0006765254,0.00006927328],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006324293,0.000004108642,2.770555e-7,0.00004144074,0.00003123151,8.040108e-7,0.00004055411,0.005475354,0.00001101433,0.912114,0.01001119,0.07226375],"study_design_scores_gemma":[0.0001428128,0.0000850216,5.636135e-7,0.0001245263,0.00002417822,0.00000232495,5.773033e-7,0.6032497,0.0001807035,0.3780501,0.01798626,0.0001531953],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[1.72959e-8,0.0009339488,0.9815952,0.00006277301,0.0003631695,0.0003246186,0.0002548731,0.00004176011,0.01642363],"genre_scores_gemma":[0.000004912446,0.00100553,0.860179,0.00006881493,0.00007625732,0.00002462982,0.00004097742,0.00002510769,0.1385748],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5977744,"threshold_uncertainty_score":0.6534177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03155202280743595,"score_gpt":0.3017830485488732,"score_spread":0.2702310257414372,"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."}}