{"id":"W2006148284","doi":"10.1002/cjs.11147","title":"A cluster‐sample approach for Monte Carlo integration using multiple samplers","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Foundation","keywords":"Estimator; Monte Carlo method; Sample (material); Statistics; Sample size determination; Cluster (spacecraft); Variance reduction; Variance (accounting); Gaussian; Monte Carlo integration; Mathematics; Computer science; Field (mathematics); Markov chain Monte Carlo; Monte Carlo molecular modeling; Physics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01318965,0.001045556,0.001983252,0.003213852,0.001141602,0.001823071,0.004555603,0.001518718,0.006674069],"category_scores_gemma":[0.04568562,0.001190398,0.001916152,0.00294511,0.00203939,0.002822867,0.00267941,0.003152973,0.001058755],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002335876,"about_ca_system_score_gemma":0.002586349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01037393,"about_ca_topic_score_gemma":0.01079255,"domain_scores_codex":[0.995535,0.002626839,0.000169697,0.0004916587,0.0010081,0.0001687709],"domain_scores_gemma":[0.974798,0.01930332,0.0008121333,0.002312458,0.00247604,0.0002979819],"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":[0.00023511,0.0001804193,0.002503033,0.0001858477,0.0003638042,0.0001788918,0.0002892151,0.48374,0.001413619,0.4023461,0.003604096,0.1049598],"study_design_scores_gemma":[0.00001843011,0.00001482595,0.0001125643,0.00001378916,0.00001662881,0.00001660593,0.000008797449,0.9604641,0.0003347936,0.03786306,0.001124725,0.00001178856],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00100936,0.00005174449,0.998394,0.00004118221,0.00001629541,0.00004346793,0.00001268771,0.000139138,0.0002920662],"genre_scores_gemma":[0.06799581,0.0001228781,0.9292802,0.0001020419,0.0001034086,0.0005423868,0.000146082,0.0002661425,0.001440947],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01318965,"threshold_uncertainty_score":0.06975436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2503426805147262,"score_gpt":0.3606109690667924,"score_spread":0.1102682885520662,"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."}}