{"id":"W4387311761","doi":"10.1080/01621459.2023.2263202","title":"Copula Modeling of Serially Correlated Multivariate Data with Hidden Structures","year":2023,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Copula (linguistics); Computer science; Multivariate statistics; Hidden Markov model; Inference; Section (typography); Algorithm; Theoretical computer science; Econometrics; Data mining; Mathematics; Artificial intelligence; Machine learning","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":[0.005953783,0.001288817,0.001905625,0.001814522,0.0006309989,0.001899096,0.003626362,0.001413274,0.002970132],"category_scores_gemma":[0.02560868,0.001273714,0.002068933,0.002281455,0.001700498,0.003253232,0.002475444,0.003242331,0.001035378],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001010166,"about_ca_system_score_gemma":0.001455151,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004824437,"about_ca_topic_score_gemma":0.004768169,"domain_scores_codex":[0.997474,0.001253166,0.0001132433,0.0006234015,0.0003491211,0.0001870729],"domain_scores_gemma":[0.9852509,0.01105968,0.001344104,0.001354855,0.0007684964,0.0002219285],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00009135102,0.00008989359,0.003290293,0.0002194466,0.0003071786,0.0003769269,0.0004813638,0.6469708,0.004411206,0.2822792,0.002481752,0.0590006],"study_design_scores_gemma":[0.000005104928,0.00001180095,0.0003678019,0.00001476112,0.00001769643,0.00004596846,0.00001231755,0.9488578,0.0004628951,0.04950645,0.0006804763,0.00001687625],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003822548,0.00009793096,0.9955555,0.00007641221,0.00001170518,0.00001256296,0.00006563197,0.0001120154,0.0002457098],"genre_scores_gemma":[0.3517821,0.001348306,0.6400602,0.0002957287,0.0001818374,0.0004258849,0.00107304,0.0005418059,0.004291099],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005953783,"threshold_uncertainty_score":0.03148705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02901518291234277,"score_gpt":0.3159927258049126,"score_spread":0.2869775428925698,"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."}}