Latent variables in choice models: Optima case study

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Choice Modeling

Explores how choice modeling predicts human behavior and quantifies preferences in various scenarios.

Regression Modeling: Variable Selection

Explores regression modeling, emphasizing the importance of interpretation and prediction in model building.

Principal Component Analysis: Properties and Applications

Explores Principal Component Analysis theory, properties, applications, and hypothesis testing in multivariate statistics.

Ingredients of choice theory

Covers the theoretical foundations of choice theory and its key components for decision making.

Choice Models: Predicting Consumer Behavior

Explores the use of choice models to predict consumer behavior in various contexts.

Introduction to ML for Behavioral Data

Covers machine learning approaches for personalization and their real-world application, emphasizing homework, projects, and feedback.

Red bus/Blue bus paradox

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

Discrete Choice Analysis

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

Topic Models: Understanding Latent Structures

Explores topic models, Gaussian mixture models, Latent Dirichlet Allocation, and variational inference in understanding latent structures within data.

Topic Models: Latent Dirichlet Allocation

Covers topic models, focusing on Latent Dirichlet Allocation, clustering, GMMs, Dirichlet distribution, LDA learning, and applications in digital humanities.