{"id":"W2800451635","doi":"10.6000/1929-6029.2018.07.02.4","title":"Bayesian Analysis of Markov Based Logistic Model","year":2018,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Bayes factor; Exponential function; Statistics; Mathematics; Bayes' theorem; Variable-order Bayesian network; Bayes estimator; Function (biology); Applied mathematics; Logistic regression; Markov model; Bayesian inference; Econometrics; Computer science; Markov chain","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":[0.006212457,0.0006768907,0.001610612,0.001874539,0.0007326186,0.001611166,0.001845126,0.001178653,0.005125733],"category_scores_gemma":[0.02269594,0.0007191786,0.00160017,0.001603099,0.0009837662,0.00267888,0.001767732,0.002404974,0.0007552843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001375636,"about_ca_system_score_gemma":0.001904953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01125252,"about_ca_topic_score_gemma":0.007860741,"domain_scores_codex":[0.9967504,0.001804955,0.0001208178,0.0004948034,0.0005659595,0.0002631366],"domain_scores_gemma":[0.9896702,0.008397214,0.0006963733,0.0003654791,0.0006666224,0.0002041432],"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":[0.000281732,0.000121989,0.009655201,0.0003259516,0.0003172648,0.000441692,0.0004125318,0.5030254,0.00111681,0.3965489,0.006025356,0.08172715],"study_design_scores_gemma":[0.00001538931,0.0000251236,0.000842208,0.00003130385,0.00002850966,0.00006782467,0.00002102359,0.9084752,0.000116584,0.08916515,0.001186834,0.00002476107],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02845463,0.001138445,0.9663173,0.0008171092,0.00006900124,0.00007136478,0.0004833779,0.0002233356,0.002425596],"genre_scores_gemma":[0.7709018,0.005391756,0.2012229,0.0004754075,0.0005441664,0.0006405305,0.003125857,0.0002601741,0.01743737],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01125252,"threshold_uncertainty_score":0.03285503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2158784806776032,"score_gpt":0.5573796766179351,"score_spread":0.3415011959403319,"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."}}