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Markov Bases in Algebraic Statistics

Overview of attention for book
Markov Bases in Algebraic Statistics
Springer New York

Table of Contents

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    Book Overview
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    Chapter 1 Exact Tests for Contingency Tables and Discrete Exponential Families
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    Chapter 2 Markov Chain Monte Carlo Methods over Discrete Sample Space
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    Chapter 3 Toric Ideals and Their Gröbner Bases
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    Chapter 4 Definition of Markov Bases and Other Bases
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    Chapter 5 Structure of Minimal Markov Bases
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    Chapter 6 Method of Distance Reduction
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    Chapter 7 Symmetry of Markov Bases
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    Chapter 8 Decomposable Models of Contingency Tables
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    Chapter 9 Markov Basis for No-Three-Factor Interaction Models and Some Other Hierarchical Models
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    Chapter 10 Two-Way Tables with Structural Zeros and Fixed Subtable Sums
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    Chapter 11 Regular Factorial Designs with Discrete Response Variables
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    Chapter 12 Groupwise Selection Models
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    Chapter 13 The Set of Moves Connecting Specific Fibers
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    Chapter 14 Disclosure Limitation Problem and Markov Basis
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    Chapter 15 Gröbner Basis Techniques for Design of Experiments
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    Chapter 16 Running Markov Chain Without Markov Bases
Attention for Chapter 12: Groupwise Selection Models
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Chapter title
Groupwise Selection Models
Chapter number 12
Book title
Markov Bases in Algebraic Statistics
Published in
Springer Series in Statistics, June 2012
DOI 10.1007/978-1-4614-3719-2_12
Book ISBNs
978-1-4614-3718-5, 978-1-4614-3719-2
Authors

Satoshi Aoki, Hisayuki Hara, Akimichi Takemura

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