Lecture

Discrete Choice Models: Selected Topics

Description

This lecture covers selected topics on discrete choice models, focusing on predicting behavior using mathematical models and exploring choice modeling to obtain disaggregate demand models. It introduces theoretical foundations, operational modeling steps, and various methodologies such as binary, multinomial, nested, and cross-nested logit models.

In MOOCs (2)
Selected Topics on Discrete Choice
Discrete choice models are used extensively in many disciplines where it is important to predict human behavior at a disaggregate level. This course is a follow up of the online course “Introduction t
Selected Topics on Discrete Choice
Discrete choice models are used extensively in many disciplines where it is important to predict human behavior at a disaggregate level. This course is a follow up of the online course “Introduction t
Instructor
ipsum amet commodo
Excepteur ipsum consequat nisi cillum officia aute ex culpa incididunt magna aliquip excepteur irure proident. Voluptate ullamco deserunt ad sit dolore duis enim ullamco sint officia do elit. Deserunt dolor consequat esse sit enim est ad ut incididunt deserunt amet nisi minim qui. Voluptate exercitation proident laboris Lorem laborum amet. Elit quis elit fugiat aute reprehenderit amet in sit ut mollit.
Login to see this section
About this result
This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.
Related lectures (34)
Mixture Models: Simulation-based Estimation
Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.
Red bus/Blue bus paradox
Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.
MLE Applications: Binary Choice Models
Explores the application of Maximum Likelihood Estimation in binary choice models, covering probit and logit models, latent variable representation, and specification tests.
Derivation of the logit modelMOOC: Introduction to Discrete Choice Models
Explains the derivation of the logit model in choice models, covering error terms, choice sets, and availability conditions.
Discrete Choice Analysis
Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.
Show more

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.