Semialgebraic statistics and 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...
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| Format: | eBook |
| Language: | English |
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Boca Raton
CRC Press
2016
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| Series: | Monographs on statistics and applied probability
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| Collection: | O'Reilly - Collection details see MPG.ReNa |
| Summary: | 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 |
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| Item Description: | "A Chapman & Hall Book." |
| Physical Description: | 245 pages illustrations |
| ISBN: | 9780429189623 0429189621 9781466576223 1466576227 |