Latent Space Models for Multiplex Networks

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Mixture Models: Simulation-based Estimation

Explores mixture models, including discrete and continuous mixtures, and their application in capturing taste heterogeneity in populations.

Latent Space Models: Inference and Applications

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

Mixture models: taste heterogeneity

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

Maximum Likelihood Theory & Applications

Covers maximum likelihood theory, applications, and hypothesis testing principles in econometrics.

Generative Models: Logistic Regression & Gaussian Distribution

Explores generative models, logistic regression, and Gaussian distribution for approximating posterior probabilities and optimizing model performance.

Generalized Linear Models: A Brief Review

Provides an overview of Generalized Linear Models, focusing on logistic and Poisson regression models, and their implementation in R.

Mixture models: alternative specific variance

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

Linear Models for Classification: Logistic Regression and SVM

Covers linear models for classification, focusing on logistic regression and support vector machines.

Gaussian Mixture Models & Noisy Signals

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

Parameter Estimation

Introduces statistical inference concepts, focusing on parameter estimation, unbiased estimators, and mean estimation using independent random variables.