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Markov Bases in Algebraic Statistics

Markov Bases in Algebraic Statistics


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About the Book

Exact tests for contingency tables and discrete exponential families.- Markov chain Monte Carlo methods over discrete sample space.- Toric ideals and their Gröbner bases.- Definition of Markov bases and other bases.- Structure of minimal Markov bases.- Method of distance reduction.- Symmetry of Markov bases.- Decomposable models of contingency tables.- Markov basis for no-three-factor interaction models and some other hierarchical models.- Two-way tables with structural zeros and fixed subtable sums.- Regular factorial designs with discrete response variables.- Group-wise selection models.- The set of moves connecting specific fibers.- Disclosure limitation problem and Markov basis.- Gröbner basis techniques for design of experiments.- Running Markov chain without Markov bases.- References.- Index.


About the Author:

Satoshi Aoki obtained his doctoral degree from the University of Tokyo in 2004 and is currently an associate professor in the Graduate School of Science and Engineering, Kagoshima University.

Hisayuki Hara obtained his doctoral degree from the University of Tokyo in 1999 and is currently an associate professor in the Faculty of Economics, Niigata University.

Akimichi Takemura obtained his doctoral degree from Stanford University in 1982 and is currently a professor in the Graduate School of Information Science and Technology, University of Tokyo.


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Product Details
  • ISBN-13: 9781461437185
  • Publisher: Springer
  • Publisher Imprint: Springer
  • Depth: 25
  • Height: 234 mm
  • No of Pages: 300
  • Series Title: Springer Statistics
  • Weight: 666 gr
  • ISBN-10: 1461437180
  • Publisher Date: 24 Jul 2012
  • Binding: Hardback
  • Edition: 2012 ed.
  • Language: English
  • Returnable: Y
  • Spine Width: 19 mm
  • Width: 156 mm


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