Introduction to Behavior Modeling: Simple Example

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Covers methods to define the design storm, empirical distribution of rainfall maxima, Gumbel distribution, and intensity-duration-frequency relationships.

Bias, Variance, Consistency, EMV

Covers bias, variance, mean squared error, consistency, and maximum likelihood estimation in the Poisson model.

Regression: Simple and Multiple Linear

Covers simple and multiple linear regression, including least squares estimation and model diagnostics.

Mixture models: taste heterogeneity

Explores mixture models in discrete choice and random parameters estimation results.

Gaussian Mixture Models & Noisy Signals

Explores Gaussian mixture models and denoising noisy signals using a probabilistic approach.

Choice models with latent variables: Modeling latent concepts

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

Estimation and Analysis of Deviance

Covers estimation of unknown parameters, analyzing model fit, bird prey response, and model diagnostics.

Recursive Least Squares

Covers the Recursive Least Squares method for parameter estimation based on observation equations and design matrices.

Google Analytics 4: Understand & set up GA4

Introduces Google Analytics 4 and guides on setting it up for WordPress websites, emphasizing key differences and event tracking.

Critical Data Studies: Introduction and Models

Introduces the Critical Data Studies course, covering data models, trust, reproducibility, biases, and ethics.