Salīdzināt metodes

	Beijesiskā regresija	Modelis ar ligzdotu logistisko izvēli (Nested Logit Discrete Choice Model)
Nozare	Bajesa metodes	Ekonometrija
Saime	Bayesian methods	Regression model
Izcelsmes gads	—	1985
Autors	—	Daniel McFadden; Ben-Akiva & Lerman
Tips	Bayesian linear model	Discrete choice regression model
Pirmavots	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	Ben-Akiva, M., & Lerman, S. R. (1985). Discrete Choice Analysis: Theory and Application to Travel Demand. MIT Press. ISBN: 978-0-262-02217-0
Citi nosaukumi	bayesian linear regression, probabilistic regression, bayesian regresyon	Tree Logit Model, Hierarchical Logit Model, Generalized Extreme Value Logit, İç İçe Logit Modeli
Saistītās	2	3
Kopsavilkums	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.	The Nested Logit model is a discrete choice framework that groups mutually exclusive alternatives into hierarchical nests, allowing correlated unobserved utilities within each nest while maintaining independence across nests. Introduced formally by Ben-Akiva and Lerman (1985) and grounded in McFadden's Generalized Extreme Value (GEV) theory, it extends the standard Multinomial Logit by relaxing the restrictive Independence of Irrelevant Alternatives assumption within predefined groups of similar alternatives.