Piotr Zwiernik

Semialgebraic Statistics and Latent Tree Models

• Gebundenes Buch

Produktbeschreibung

This book 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 introduces key concepts in algebraic statistics, focusing on methods that are helpful in the study of models with hidden variables. The second part illustrates important examples of tree models with hidden variables. The author develops the important concepts of L-cumulants and links latent tree models and various tree spaces.

Produktdetails

• Verlag: CRC Press
• Seitenzahl: 246
• Erscheinungstermin: 21. August 2015
• Englisch
• Abmessung: 234mm x 157mm x 18mm
• Gewicht: 476g
• ISBN-13: 9781466576216
• ISBN-10: 1466576219
• Artikelnr.: 40905740

Autorenporträt

Piotr Zwiernik is a Marie Sk¿odowska-Curie International Fellow in the Department of Mathematics at the University of Genoa. His research interests include statistical inference, graphical models with hidden variables, algebraic statistics, singular learning theory, time series analysis, and symbolic methods. He received a PhD in statistics from the University of Warwick.

Inhaltsangabe

Introduction. Semialgebraic statistics: Algebraic and analytic geometry.
Algebraic statistical models. Tensors, moments, and combinatorics. Latent
tree graphical models: Phylogenetic trees and their models. The local
geometry. The global geometry. Gaussian latent tree models.