Latent Space Models: Inference and Applications

This lecture by the instructor covers latent space models, focusing on the assumptions of conditional independence and parameterization using logistic regression. It delves into the representation of networks in latent spaces, the relationship between nodes, and the equivalence to other modeling frameworks. The lecture also explores random dot product graphs, stochastic block models, and spectral decompositions. Additionally, it discusses the latent class model, latent distance model, and latent eigenmodel, highlighting their applications in network data analysis. The presentation concludes with a detailed explanation of the profile likelihood method for parameter estimation in latent space models.