{"id":"W2037535649","doi":"10.1016/j.spl.2009.04.011","title":"Modelling heavy-tailed count data using a generalised Poisson-inverse Gaussian family","year":2009,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":45,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; McMaster University","funders":"","keywords":"Mathematics; Poisson distribution; Inverse Gaussian distribution; Count data; Statistics; Gaussian; Exponential family; Inverse; Applied mathematics; Probability density function; Statistical physics; Distribution (mathematics); Mathematical analysis","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02529376,0.001771087,0.005041624,0.004887921,0.001425413,0.004640993,0.009614882,0.006322918,0.003843033],"category_scores_gemma":[0.06774782,0.002879864,0.00594938,0.005917862,0.004794043,0.007775009,0.004618349,0.006063416,0.002003766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002473145,"about_ca_system_score_gemma":0.002562873,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01106008,"about_ca_topic_score_gemma":0.007233613,"domain_scores_codex":[0.9876495,0.006824601,0.0006723452,0.002158654,0.002019727,0.0006751199],"domain_scores_gemma":[0.958933,0.03142237,0.002797571,0.003343529,0.002859192,0.0006442805],"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":[0.000295113,0.0001227071,0.005641797,0.0005436866,0.0005428797,0.0006052494,0.0009191418,0.533951,0.001607044,0.4038667,0.003761195,0.04814353],"study_design_scores_gemma":[0.00004422624,0.00003631413,0.0007493231,0.00006592437,0.00008442371,0.0002906496,0.00005985282,0.8145708,0.0002885109,0.1820772,0.001650447,0.00008229928],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004954239,0.0002598102,0.9937811,0.0002270411,0.00005155284,0.00004767731,0.0001393554,0.0001874014,0.0003518379],"genre_scores_gemma":[0.3154176,0.003081109,0.665493,0.0008760415,0.0005774333,0.001128079,0.002068509,0.0006960511,0.01066213],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02529376,"threshold_uncertainty_score":0.1337677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0909370416795725,"score_gpt":0.3058885405100507,"score_spread":0.2149514988304781,"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."}}