{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008693392,0.0002035449,0.0006265016,0.0005134065,0.0002100551,0.0002346592,0.0006106793,0.0001094929,0.00006282676],"category_scores_gemma":[0.00006013974,0.0001678193,0.0007209729,0.001329191,0.00004552255,0.001758268,0.000065525,0.000171579,0.00001185648],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001100944,"about_ca_system_score_gemma":0.0002122332,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001290542,"about_ca_topic_score_gemma":0.00002670975,"domain_scores_codex":[0.9984096,0.0000824763,0.0005935012,0.0002631611,0.000346089,0.0003051375],"domain_scores_gemma":[0.9984703,0.00004126696,0.0005151674,0.0003195788,0.000482933,0.0001707338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006929147,0.0004177127,0.0003354501,0.00008413629,0.006692524,0.00005211528,0.002682804,0.8515381,0.02972807,0.06830773,0.008662157,0.03080627],"study_design_scores_gemma":[0.0006446338,0.0006365578,0.00008463266,0.00002516195,0.001304277,0.0002274536,0.00001759869,0.8216758,0.009597955,0.1639903,0.001428383,0.0003672381],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001425155,0.0001196004,0.9945813,0.003314971,0.00006979218,0.00008602874,0.000009955632,0.00004008132,0.0003531451],"genre_scores_gemma":[0.01943035,0.00005914666,0.9759858,0.0001899879,0.0001723739,0.000004133545,0.000004783737,0.0000165184,0.004136941],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.09568257,"threshold_uncertainty_score":0.6843473,"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."}}