Latent Space Models: Inference and Applications

Related lectures (30)

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Covers latent space models, logistic regression, RDPG, and real-world networks.

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

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

Covers variational inference and neural networks for classification tasks.

Covers logistic regression, likelihood function, Newton's method, and classification error estimation.

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

Explores logistic regression model interpretation, parameter estimation, and model comparison using likelihood ratio tests.

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

Explores logistic regression analysis of horseshoe crab data, focusing on odds ratio interpretation and model fitting.

Covers supervised learning through likelihood maximization to find optimal parameters.