{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005315398,0.001082678,0.001095388,0.002070726,0.001331517,0.004756906,0.001344539,0.0042338,0.01583515],"category_scores_gemma":[0.01977405,0.000617468,0.0006930421,0.002245559,0.004036469,0.007282276,0.001776328,0.006152038,0.00417024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003502715,"about_ca_system_score_gemma":0.002132502,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003963173,"about_ca_topic_score_gemma":0.002309101,"domain_scores_codex":[0.9975433,0.001122072,0.00009274363,0.0003708023,0.0007634375,0.0001075931],"domain_scores_gemma":[0.9954656,0.003274333,0.0002236862,0.0003201072,0.0005951606,0.0001212498],"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":[0.00000241558,0.000003964702,0.00006929802,0.00002719952,0.000004143124,0.00001650845,0.00004163875,0.001399724,0.00002842353,0.9795328,0.006137193,0.01273668],"study_design_scores_gemma":[0.000002265201,0.00000282989,0.00005354394,0.00004493544,0.000002334708,0.00002557316,0.00001516469,0.003639091,0.00002155198,0.9703744,0.02581333,0.000005005165],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.003192114,0.03196089,0.7343085,0.03293244,0.002007277,0.0001117742,0.0006296943,0.0003048504,0.1945524],"genre_scores_gemma":[0.3393895,0.07136371,0.4170559,0.007814747,0.01150795,0.001096161,0.0009684482,0.0005319726,0.1502717],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01583515,"threshold_uncertainty_score":0.05297381,"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."}}