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 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 distributions,
develops new combinatorial tools to study models with hidden data,
and describes the semialgebraic structure of statistical models.
The second part illustrates important examples of tree models with
hidden variables. The book discusses the underlying models and
related combinatorial concepts of phylogenetic trees as well as the
local and global geometry of latent tree models. It also extends
previous results to Gaussian latent tree models. This book shows
you how both combinatorics and algebraic geometry enable a better
understanding of latent tree models. It contains many results on
the geometry of the models, including a detailed analysis of
identifiability and the defining polynomial constraints.
General
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
Welcome to Loot.co.za!
Sign in / Register |Wishlists & Gift Vouchers |Help | Advanced search
