Discrete Choice Analysis

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (31)

Page 1 of 4

Discrete Choice Analysis

Introduces Discrete Choice Analysis, covering scale, depth, data collection, and statistical inference.

Document Analysis: Topic Modeling

Explores document analysis, topic modeling, and generative models for data generation in machine learning.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Discrete choice and machine learning: two methodologies

Delves into the complementary methodologies of discrete choice and machine learning, covering notations, variables, models, data processes, extrapolation, what-if analysis, and more.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Specification Testing and Machine Learning

Explores specification testing, machine learning, overfitting, regularization, prediction tests, and variable selection.

Red bus/Blue bus paradox

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Linear Regression: Statistical Inference and Regularization

Covers the probabilistic model for linear regression and the importance of regularization techniques.

Choice models with latent variables: Modeling latent concepts

Explores choice models with latent variables and their estimation process based on likelihood integration.

Regression Trees and Ensemble Methods in Machine Learning

Discusses regression trees, ensemble methods, and their applications in predicting used car prices and stock returns.