Algebraic statistics

Bibliographic Information

Algebraic statistics

Seth Sullivant

(Graduate studies in mathematics, v. 194)

American Mathematical Society, c2018

Available at  / 28 libraries

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Includes bibliographical references (p. 463-480) and index

Description and Table of Contents

Description

Algebraic statistics uses tools from algebraic geometry, commutative algebra, combinatorics, and their computational sides to address problems in statistics and its applications. The starting point for this connection is the observation that many statistical models are semialgebraic sets. The algebra/statistics connection is now over twenty years old, and this book presents the first broad introductory treatment of the subject. Along with background material in probability, algebra, and statistics, this book covers a range of topics in algebraic statistics including algebraic exponential families, likelihood inference, Fisher's exact test, bounds on entries of contingency tables, design of experiments, identifiability of hidden variable models, phylogenetic models, and model selection. With numerous examples, references, and over 150 exercises, this book is suitable for both classroom use and independent study.

Table of Contents

Introduction Probability Primer Algebra Primer Conditional Independence Statistics Primer Exponential Families Likelihood Inference The Cone of Sufficient Statistics Fisher's Exact Test Bounds on Cell Entries Exponential Random Graph Models Design of Experiments Graphical Models Hidden Variables Phylogenetic Models Identifiability Model Selection and Bayesian Integrals MAP Estimation and Parametric Inference Finite Metric Spaces Bibliography Index.

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