{"id":"W2126380140","doi":"10.1002/9780470404324.hof003064","title":"Bayesian Probability for Investors","year":2008,"lang":"en","type":"other","venue":"Handbook of Finance","topic":"Forecasting Techniques and Applications","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"WiLAN (Canada)","funders":"","keywords":"Gibbs sampling; Markov chain Monte Carlo; Bayesian probability; Posterior probability; Prior probability; Conjugate prior; Bayes factor; Bayesian hierarchical modeling; Bayes' theorem; Marginal likelihood; Computer science; Bayesian statistics; Conditional probability; Probability distribution; Bayesian inference; Econometrics; Mathematics; Statistics; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004301358,0.0001537579,0.0003878993,0.0001594181,0.00006263813,0.00001499395,0.0006712752,0.0001959819,0.0002630244],"category_scores_gemma":[0.0006168163,0.0001214224,0.0001627879,0.0002632483,0.0003073876,0.00002666122,0.00006051255,0.0000810122,0.00004380567],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001881265,"about_ca_system_score_gemma":0.0001184265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005729928,"about_ca_topic_score_gemma":0.00008663093,"domain_scores_codex":[0.9985504,0.00002287808,0.0004438056,0.0004594312,0.0003627989,0.0001606567],"domain_scores_gemma":[0.9981302,0.000219881,0.000588135,0.0008979445,0.0001286073,0.00003524716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004463943,0.00002879274,0.0001068261,0.00002214004,0.000002799563,2.582403e-7,0.00002643465,0.000002799881,0.00001505001,0.00545785,0.9744118,0.01992077],"study_design_scores_gemma":[0.00008740692,0.00005246359,0.00003556393,0.0002861384,0.000003909796,0.000002021301,0.000001247555,0.0001766448,0.0008781602,0.07910489,0.9192494,0.0001221518],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00007476174,0.00430011,0.5648705,0.0002641141,0.0002199431,0.00202029,0.0004496311,0.0002657328,0.4275349],"genre_scores_gemma":[0.0004059271,0.0005751819,0.3646572,0.00005017246,0.0001238033,0.0003236691,0.00001065883,0.0001198915,0.6337336],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.2061987,"threshold_uncertainty_score":0.495146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1671223332955788,"score_gpt":0.374859508792923,"score_spread":0.2077371754973442,"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."}}