{"id":"W2056439621","doi":"10.1111/j.1467-9892.2004.01874.x","title":"Bayesian Subset Model Selection for Time Series","year":2004,"lang":"en","type":"article","venue":"Journal of Time Series Analysis","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"SETAR; Autoregressive model; Series (stratigraphy); Bayesian probability; Nonlinear autoregressive exogenous model; Model selection; STAR model; Selection (genetic algorithm); Markov chain; Time series; Bilinear interpolation; Markov chain Monte Carlo; Econometrics; Mathematics; Computer science; Algorithm; Autoregressive integrated moving average; Statistics; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01004385,0.001255646,0.002975914,0.002218463,0.000813365,0.001711003,0.001791654,0.001263183,0.003340052],"category_scores_gemma":[0.02440985,0.001278059,0.001807183,0.001803777,0.001231109,0.001871483,0.001800287,0.001692759,0.0008168226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009804051,"about_ca_system_score_gemma":0.001334579,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002421909,"about_ca_topic_score_gemma":0.00245635,"domain_scores_codex":[0.9939903,0.00411311,0.0001776219,0.0006441833,0.0008978831,0.0001768122],"domain_scores_gemma":[0.9842518,0.01225378,0.0007696502,0.00113161,0.001328063,0.0002650572],"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":[0.0004198683,0.0001571067,0.004570505,0.0002347599,0.0006469459,0.0002434876,0.000309409,0.7078465,0.001880774,0.1091596,0.005130824,0.1694001],"study_design_scores_gemma":[0.00002323086,0.00003592444,0.0004008637,0.00001878222,0.00003125661,0.00003101907,0.00001607906,0.9468658,0.0003538293,0.0512631,0.0009495483,0.00001060372],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0137627,0.0002333053,0.9846949,0.0001834991,0.00002360375,0.0000611608,0.0001266251,0.0001865229,0.0007276596],"genre_scores_gemma":[0.5663163,0.0008744871,0.4223491,0.0002929333,0.0002279887,0.001183729,0.003018749,0.0003243069,0.005412417],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01004385,"threshold_uncertainty_score":0.05311757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008030792056920965,"score_gpt":0.2477919867874861,"score_spread":0.2397611947305651,"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."}}