By Piotr Zwiernik
Semialgebraic records and Latent Tree Models explains how you can research statistical types with hidden (latent) variables. It takes a scientific, geometric method of learning the semialgebraic constitution of latent tree models.
The first a part of the e-book supplies a common advent to key recommendations in algebraic records, concentrating on equipment which are worthy within the examine of types with hidden variables. the writer makes use of tensor geometry as a average language to accommodate multivariate likelihood distributions, develops new combinatorial instruments to check types with hidden info, and describes the semialgebraic constitution of statistical models.
The moment half illustrates very important examples of tree versions with hidden variables. The publication discusses the underlying types and comparable combinatorial suggestions of phylogenetic timber in addition to the neighborhood and international geometry of latent tree types. It additionally extends prior effects to Gaussian latent tree models.
This e-book indicates you the way either combinatorics and algebraic geometry permit a greater figuring out of latent tree versions. It includes many effects at the geometry of the versions, together with an in depth research of identifiability and the defining polynomial constraints.
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Extra resources for Semialgebraic Statistics and Latent Tree Models (Chapman & Hall/CRC Monographs on Statistics & Applied Probability)
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Semialgebraic Statistics and Latent Tree Models (Chapman & Hall/CRC Monographs on Statistics & Applied Probability) by Piotr Zwiernik
by Jason
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