Mixture models: latent classes

Related lectures (29)

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

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

Explores the panel effect model with fixed and random effects, discussing estimation challenges and the impact of serial correlation.

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

Explains confidence intervals, parameter estimation methods, and the central limit theorem in statistical inference.

Covers the linear mixed model, including fixed and random effects, estimation, and inference techniques.

Introduces linear regression, generalized linear models, and mixed-effect models for regression analysis.

Covers variational inference and neural networks for classification tasks.

Explores latent space models for multiplex networks, discussing network data representation, probabilistic models, and shared structure assumptions.

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