เปรียบเทียบวิธี

	แบบจำลองโลจิตแบบผสม	การถดถอยแบบเบย์ (Bayesian Regression)
สาขาวิชา	เศรษฐมิติ	เบย์
ตระกูล	Regression model	Bayesian methods
ปีกำเนิด	2000	—
ผู้ริเริ่ม	Daniel McFadden & Kenneth Train	—
ประเภท	Random-parameters discrete choice model	Bayesian linear model
แหล่งต้นตำรับ	Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge University Press. ISBN: 978-0-521-74738-7	Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955
ชื่อเรียกอื่น	Random Parameters Logit, Mixed Multinomial Logit, Error Components Logit, Karma Logit Modeli	bayesian linear regression, probabilistic regression, bayesian regresyon
ที่เกี่ยวข้อง	3	2
สรุป	The Mixed Logit model, introduced formally by McFadden and Train (2000) and elaborated in Train (2009), is a flexible discrete choice framework that allows preference parameters to vary randomly across decision-makers. By integrating standard logit probabilities over a mixing distribution of coefficients, it overcomes the restrictive independence of irrelevant alternatives (IIA) property and accommodates unobserved taste heterogeneity, panel data correlation, and complex substitution patterns across alternatives.	Bayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.