{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006499295,0.0009807266,0.001549242,0.002664018,0.0009682038,0.003502849,0.003257676,0.002118316,0.008062053],"category_scores_gemma":[0.01655206,0.0009396404,0.002383192,0.00240488,0.003843834,0.005381604,0.003431125,0.003085826,0.001930255],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00193097,"about_ca_system_score_gemma":0.001262518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006490286,"about_ca_topic_score_gemma":0.004501926,"domain_scores_codex":[0.9961494,0.00166413,0.0001815364,0.0009004751,0.0007619622,0.0003424039],"domain_scores_gemma":[0.9931105,0.003176241,0.0007513822,0.001286141,0.001368747,0.000306876],"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":[0.00007709814,0.00002880629,0.002714753,0.00009814693,0.00009951014,0.0002557655,0.0005507288,0.06923864,0.001357449,0.8923046,0.003429232,0.0298454],"study_design_scores_gemma":[0.00002458875,0.00004170354,0.001121535,0.00007186457,0.00004546919,0.0003188112,0.0001759186,0.4948973,0.0006687117,0.4937666,0.008791758,0.00007569635],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01978551,0.0005087234,0.9741398,0.000652282,0.0001094952,0.000100996,0.0002735627,0.0002246292,0.004205011],"genre_scores_gemma":[0.6331345,0.001667327,0.3330334,0.001208333,0.000429804,0.0005058715,0.001131431,0.0003963234,0.02849301],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008062053,"threshold_uncertainty_score":0.03437197,"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."}}