{"id":"W3082702514","doi":"10.1007/s11538-021-00920-5","title":"Variance Reduction with Array-RQMC for Tau-Leaping Simulation of Stochastic Biological and Chemical Reaction Networks","year":2021,"lang":"en","type":"preprint","venue":"Bulletin of Mathematical Biology","topic":"Markov Chains and Monte Carlo Methods","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Ministerio de Economía y Competitividad; European Regional Development Fund; European Science Foundation; Eusko Jaurlaritza; Canada Research Chairs; Ministerio de Ciencia, Innovación y Universidades","keywords":"Variance reduction; Sorting; Markov chain Monte Carlo; Markov chain; Variance (accounting); Estimator; Monte Carlo method; Computer science; Sample (material); Reduction (mathematics); Algorithm; Function (biology); Mathematics; Mathematical optimization; Statistics; Physics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002685101,0.0007676014,0.001716987,0.0008599664,0.0008254549,0.001117312,0.002383662,0.001805825,0.004322607],"category_scores_gemma":[0.01272999,0.00081897,0.001393127,0.000787916,0.001241025,0.001108198,0.001706925,0.002931428,0.0007033186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001373182,"about_ca_system_score_gemma":0.001908572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008754421,"about_ca_topic_score_gemma":0.006767783,"domain_scores_codex":[0.9990193,0.0005250394,0.00003341199,0.0001231997,0.0002096477,0.00008947663],"domain_scores_gemma":[0.9925408,0.005700991,0.0002668597,0.0006524924,0.0005996233,0.0002393407],"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":[0.00007929152,0.00004865579,0.0004985846,0.00004800819,0.00005409002,0.00005539998,0.00005043071,0.9421645,0.0009123993,0.04684965,0.0007380573,0.008500778],"study_design_scores_gemma":[0.000003551582,0.000002610394,0.00001276082,0.000001359532,0.000002060964,0.000002000336,0.000001125791,0.9943845,0.0001175955,0.005394374,0.00007609282,0.000001985466],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02269206,0.0002130056,0.973065,0.000321486,0.00007209141,0.00004477376,0.0001341036,0.00119464,0.002262988],"genre_scores_gemma":[0.5955396,0.0002079502,0.3978808,0.0003563135,0.0001286959,0.0005743509,0.0004593841,0.001149799,0.00370309],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008754421,"threshold_uncertainty_score":0.01740694,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07289785100481763,"score_gpt":0.3414312360228839,"score_spread":0.2685333850180663,"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."}}