{"id":"W3124547437","doi":"10.1920/wp.cem.2008.2308","title":"A Bayesian mixed logit-probit model for multinomial choice","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Consumer Market Behavior and Pricing","field":"Business, Management and Accounting","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Multinomial probit; Ordered probit; Multinomial logistic regression; Mixed logit; Econometrics; Probit; Bayesian probability; Statistics; Probit model; Multinomial distribution; Multivariate probit model; Logit; Mathematics; Logistic regression","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":["metaepi_narrow","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0005358785,0.0004970701,0.0005601587,0.0002820786,0.0005021292,0.001459266,0.001037342,0.0004082077,0.0002907852],"category_scores_gemma":[0.0003921253,0.0004739101,0.0004137827,0.00005130935,0.0000652005,0.0005630923,0.001911284,0.0004810038,0.00008148792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004237016,"about_ca_system_score_gemma":0.0001053803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003627864,"about_ca_topic_score_gemma":0.002768194,"domain_scores_codex":[0.9979712,0.000008217931,0.0004515162,0.0008295559,0.000218921,0.0005205821],"domain_scores_gemma":[0.9979242,0.0001127296,0.0005795641,0.001075877,0.0002827775,0.00002484717],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009057495,0.0007827993,0.1379174,0.008871031,0.0005753197,0.00003890706,0.0002821872,0.01665398,0.001091739,0.00774671,0.1522093,0.6729249],"study_design_scores_gemma":[0.001116595,0.000002706423,0.007080081,0.0001582642,0.0004296264,5.706457e-7,0.00001678915,0.9572279,0.00002122225,0.005192131,0.02793966,0.0008144351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.175529,0.0002905348,0.5777007,0.004388206,0.01011523,0.007242875,0.0001660735,0.001793834,0.2227736],"genre_scores_gemma":[0.9809462,0.000007245369,0.005906024,0.0006855387,0.002362254,0.0003933999,0.0002606623,0.00009867576,0.009339944],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9405739,"threshold_uncertainty_score":0.9997712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07771451459296644,"score_gpt":0.2979548870398145,"score_spread":0.2202403724468481,"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."}}