Generative Models: Logistic Regression & Gaussian Distribution

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Explores latent space models, network representations, spectral decompositions, and parameter estimation methods.

Explores the nested logit model for discrete choice and its implications on choice behavior and parameter estimation.

Explores logistic regression for predicting vegetation proportions in the Amazon region through remote sensing data analysis.

Explores alternative specific variance in mixture models and discusses identification issues and model comparisons using 500 draws.

Explores estimating parameters through confidence intervals in linear regression and statistics.

Explores non-parametric statistics, Bayesian methods, and linear regression with a focus on kernel density estimation and posterior distribution.

Covers logistic regression's probabilistic interpretation, multinomial regression, KNN, hyperparameters, and curse of dimensionality.

Covers supervised learning through likelihood maximization to find optimal parameters.

Explores maximum likelihood inference, model selection, and comparing models using likelihood ratios.

Explores maximum likelihood inference, comparing models based on likelihood ratios and demonstrating with a coin example.