{"id":"W4327623163","doi":"10.3390/jrfm16030203","title":"Bayesian Statistics for Loan Default","year":2023,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Bayesian probability; Prior probability; Interpretability; Analytics; Computer science; Bayesian inference; Econometrics; Bayesian statistics; Loan; Inference; Posterior probability; Artificial intelligence; Machine learning; Economics; Data science; Finance","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":[0.008580199,0.0007173557,0.001362043,0.003201837,0.0009158074,0.002628276,0.001537924,0.002320228,0.005093363],"category_scores_gemma":[0.05902741,0.0007308348,0.00115222,0.003459454,0.002555796,0.004523634,0.001604432,0.00447737,0.001301834],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00221135,"about_ca_system_score_gemma":0.001963406,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009167721,"about_ca_topic_score_gemma":0.005488301,"domain_scores_codex":[0.9963863,0.001923787,0.0001778647,0.0005005922,0.0008304904,0.0001810739],"domain_scores_gemma":[0.971302,0.024725,0.001277543,0.001052018,0.001381125,0.0002623943],"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":[0.0000406194,0.00003213152,0.002317084,0.0001691594,0.00006987748,0.00007456879,0.0001431852,0.133128,0.000286789,0.7908219,0.006071791,0.0668449],"study_design_scores_gemma":[0.00001384604,0.00001805798,0.00122116,0.00007789224,0.00001446038,0.00005804281,0.0000251096,0.24271,0.00009843057,0.7508749,0.004859826,0.00002828675],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008632667,0.007042593,0.9727724,0.003086579,0.0002114832,0.00004716489,0.0006586824,0.0002550101,0.007293475],"genre_scores_gemma":[0.6079978,0.0265293,0.3425038,0.00153884,0.003753213,0.0006258058,0.002998839,0.0002791321,0.01377319],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009167721,"threshold_uncertainty_score":0.04537696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008661504999755267,"score_gpt":0.2171091258195645,"score_spread":0.2084476208198092,"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."}}