Semialgebraic Statistics and Latent Tree Models explains how to analyze statistical models with hidden (latent) variables. It takes a systematic, geometric approach to studying the semialgebraic structure of latent tree models. The first part of the book gives a general introduc…
The first part of the book gives a general introduction to key concepts in algebraic statistics, focusing on methods that are helpful in the study of models with hidden variables. The author uses tensor geometry as a natural language to deal with multivariate probability distrib…
Based on material presented at the 22nd Conference of the Society for Multivariate Analysis in the Behavioral Sciences (July 2000), these 12 papers describe original research and developments concerned with latent variable modelling and structural equation modelling.
