{"id":"W1755019093","doi":"10.1002/cjs.11246","title":"A mixture of generalized hyperbolic distributions","year":2015,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":188,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mixture model; Generalized inverse Gaussian distribution; Skew; Expectation–maximization algorithm; Cluster analysis; Generalized normal distribution; Mathematics; Gaussian; Applied mathematics; Mixture distribution; Multivariate statistics; Inverse distribution; Estimation theory; Inverse Gaussian distribution; Probability distribution; Distribution (mathematics); Statistics; Probability density function; Computer science; Heavy-tailed distribution; Normal distribution; Gaussian process; Maximum likelihood; Gaussian random field; Mathematical analysis; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003970255,0.0000787313,0.0002124014,0.0001402542,0.00004715914,0.00005054902,0.0004672463,0.00005078143,0.00001230251],"category_scores_gemma":[0.0003311382,0.00006806271,0.00004673137,0.0002300585,0.00007223501,0.0001254801,0.0000153499,0.0001474651,0.000002365121],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008070122,"about_ca_system_score_gemma":0.002230659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009420893,"about_ca_topic_score_gemma":0.001780374,"domain_scores_codex":[0.9991488,0.00009228957,0.0003167595,0.00007645611,0.000165445,0.0002002315],"domain_scores_gemma":[0.998098,0.00005354826,0.0002132937,0.0002006188,0.0006037947,0.0008307154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000003273919,0.00001140813,0.0002562269,0.00000860265,0.00002737577,0.0002049362,0.0007850758,0.00003462331,0.0001185234,0.8976118,0.06739569,0.03354247],"study_design_scores_gemma":[0.001489288,0.0004082291,0.002309088,0.00008340566,0.00008665471,0.0009924215,0.0000550485,0.009800901,0.00116715,0.8667476,0.1165001,0.0003600375],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001342156,0.0006762969,0.9960006,0.0006358651,0.0005141838,0.00003402782,0.0002972911,0.000003077665,0.0004965115],"genre_scores_gemma":[0.1483334,0.00001461577,0.8513207,0.0001444088,0.00008858381,3.994939e-7,0.00000435427,0.000005115824,0.00008838029],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1469913,"threshold_uncertainty_score":0.3957093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03640317111091929,"score_gpt":0.2600045938295232,"score_spread":0.2236014227186039,"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."}}