{"id":"W2061243088","doi":"10.5539/ibr.v1n4p49","title":"Bayesian Approach for ARMA Process and Its Application","year":2009,"lang":"en","type":"article","venue":"International Business Research","topic":"Evaluation Methods in Various Fields","field":"Environmental Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shanghai Municipal Education Commission","keywords":"Bayesian probability; Computer science; Autoregressive–moving-average model; Econometrics; Process (computing); Set (abstract data type); Data mining; Artificial intelligence; Autoregressive model; Mathematics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002377412,0.0009335592,0.001017882,0.001516959,0.0006914872,0.001462255,0.001123108,0.00160688,0.005636909],"category_scores_gemma":[0.006574329,0.0005711624,0.001416491,0.001608544,0.000879346,0.001836258,0.001139371,0.002498788,0.001312326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001071543,"about_ca_system_score_gemma":0.001311327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006117311,"about_ca_topic_score_gemma":0.003588404,"domain_scores_codex":[0.9977428,0.0009766411,0.0001242443,0.0003905277,0.000663151,0.0001026926],"domain_scores_gemma":[0.9984515,0.0009695672,0.0001067949,0.00007642057,0.0003573539,0.0000384151],"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":[0.00005032075,0.00006488877,0.001080738,0.0003212197,0.0001558758,0.0002402258,0.0003115134,0.3122399,0.002652036,0.4977551,0.004420868,0.1807073],"study_design_scores_gemma":[0.00001207477,0.00003112492,0.000331484,0.00004688617,0.00003434396,0.0000978474,0.00003463657,0.8309439,0.0005615919,0.1587478,0.009124157,0.00003418796],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0008844947,0.0007602519,0.9950173,0.0002258998,0.00004511293,0.00001419281,0.00002951544,0.00006818322,0.002955015],"genre_scores_gemma":[0.37422,0.01083462,0.5910824,0.0006126144,0.001079698,0.0006171339,0.0004993813,0.0002854646,0.02076872],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006117311,"threshold_uncertainty_score":0.0188573,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09924681006405513,"score_gpt":0.4635943916155825,"score_spread":0.3643475815515274,"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."}}