{"id":"W2899063537","doi":"10.1145/3317605","title":"Infinite Swapping using IID Samples","year":2019,"lang":"en","type":"preprint","venue":"ACM Transactions on Modeling and Computer Simulation","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Defense Advanced Research Projects Agency; U.S. Department of Energy; Wind Energy Technologies Office; National Science Foundation","keywords":"Rare events; Event (particle physics); Estimator; Scaling; Construct (python library); Variance (accounting); Variance reduction; Sampling (signal processing); Importance sampling; Mathematics; Statistical physics; Statistics; Variance components; Computer science; Algorithm; Applied mathematics; Monte Carlo method; Physics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001338858,0.0003410507,0.0005196895,0.0005685479,0.0004572128,0.000616928,0.0007126469,0.0004194904,0.00003185397],"category_scores_gemma":[0.0001170646,0.0002971677,0.0002604377,0.0002746533,0.00004114328,0.0003718195,0.000144292,0.0007459076,0.00003236076],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007905676,"about_ca_system_score_gemma":0.0001364601,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001516165,"about_ca_topic_score_gemma":0.00001648327,"domain_scores_codex":[0.9966076,0.0002672537,0.000914687,0.001100486,0.0008438861,0.0002660876],"domain_scores_gemma":[0.9962096,0.001478076,0.0002592391,0.001533918,0.0004146908,0.0001044704],"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":[0.00003485395,0.0000457907,0.00005059559,0.0000272994,0.00003302057,3.485075e-7,0.00071117,0.9402608,0.000006873312,0.00005157775,0.00000231194,0.05877535],"study_design_scores_gemma":[0.0002416897,0.00003772719,0.00004957388,0.0001757187,0.0000440122,0.000002050509,0.00003722503,0.8604015,0.000006582536,0.1386382,0.00008199699,0.0002837065],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2670845,0.00009171731,0.7312585,0.0001925542,0.0009154819,0.0003040885,0.00003307456,0.00009178458,0.00002834021],"genre_scores_gemma":[0.9115031,0.00005753397,0.08800881,0.0001807158,0.0001590494,0.000007196319,0.00001538675,0.0000225702,0.00004567066],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6444186,"threshold_uncertainty_score":0.999948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3208598936038941,"score_gpt":0.4037750377499834,"score_spread":0.0829151441460893,"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."}}