{"id":"W2571051305","doi":"10.1080/00949655.2016.1277428","title":"A backward construction and simulation of correlated Poisson processes","year":2017,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bivariate analysis; Mathematics; Joint probability distribution; Poisson distribution; Conditional independence; Conditional probability distribution; Negative binomial distribution; Copula (linguistics); Independence (probability theory); Poisson regression; Univariate; Statistics; Applied mathematics; Econometrics; Multivariate statistics","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":[0.003095609,0.000489969,0.00076342,0.000762747,0.0006247316,0.0008592505,0.00129417,0.001098368,0.003053488],"category_scores_gemma":[0.01034288,0.0006398936,0.0012324,0.0008717627,0.00100789,0.001166656,0.002132586,0.001431317,0.0004572227],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006867452,"about_ca_system_score_gemma":0.001731843,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003699471,"about_ca_topic_score_gemma":0.00166151,"domain_scores_codex":[0.9987596,0.0006433098,0.00004385567,0.0001241713,0.0003383688,0.00009070136],"domain_scores_gemma":[0.9967111,0.001976241,0.0002375081,0.0004206447,0.000490487,0.0001639753],"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":[0.00007760139,0.00005612311,0.002208749,0.00003500795,0.00002462528,0.0002098313,0.0001771577,0.6986435,0.002463751,0.2839261,0.0004877308,0.01168983],"study_design_scores_gemma":[0.00001361348,0.00001457095,0.00007900377,0.000006732787,0.000003657705,0.00003040141,0.000005746821,0.9700788,0.0005581352,0.02846332,0.0007380691,0.000007960703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01782384,0.00004281193,0.9802089,0.0001068701,0.0000300981,0.00003741177,0.00005624658,0.0001040531,0.00158978],"genre_scores_gemma":[0.5526406,0.0002775908,0.441759,0.0001208484,0.0000478888,0.0003424435,0.0003933598,0.0001072841,0.004310956],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003699471,"threshold_uncertainty_score":0.01637137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1101664687383018,"score_gpt":0.4309592424271896,"score_spread":0.3207927736888878,"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."}}