Markov Bases in Algebraic Statistics by Satoshi Aoki
Algebraic statistics is a rapidly developing field, where ideas from statistics and algebra meet and stimulate new research directions.-
The Elements of Statistical Learning
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Dragons of Winter Night
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Functional Data Analysis
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Time Series: Theory and Methods
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Modeling Discrete Time-to-Event Data
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Targeted Learning
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Models for Discrete Longitudinal Data
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Regression Modeling Strategies
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Targeted Learning in Data Science
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An Introduction to Sequential Monte Carlo
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Modern Multidimensional Scaling
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Correlation Theory of Stationary and Related Random Functions
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Mathematical Statistics
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Breakthroughs in Statistics
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Annotated Readings in the History of Statistics
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Statistical Models Based on Counting Processes
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Hidden Markov Processes and Adaptive Filtering
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Design of Observational Studies
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Chaos: A Statistical Perspective
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A Comparison of the Bayesian and Frequentist Approaches to Estimation
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Smoothing Spline ANOVA Models
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Analysis of Neural Data
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Finite Mixture and Markov Switching Models
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The Gini Methodology
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Ten Projects in Applied Statistics
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Growth Curve Models and Statistical Diagnostics
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Statistical Methods in Software Engineering
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Gaussian and Non-Gaussian Linear Time Series and Random Fields
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Data
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A Course on Point Processes
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Shrinkage Estimation
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Theory of Statistics
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Bayesian and Frequentist Regression Methods
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A Statistical Model
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Prediction Theory for Finite Populations
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Tools for Statistical Inference
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Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series
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ARMA Model Identification
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Statistical Design and Analysis for Intercropping Experiments
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Approximate Distributions of Order Statistics
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Robust Asymptotic Statistics
From the reviews:
“The book by Aoki, Hara, and Takemura presents a thorough introduction to Markov chain Monte Carlo tests for discrete exponential families, focusing on the concept of Markov basesIt is an authoritative and highly readable account of this field. … This text is the definitive reference on the subject, aimed principally at statisticians interested in Markov chain algorithms for sampling from discrete exponential families and its various applications … . It could also be used as a textbook for an advanced seminar on the subject.” (Luis David García-Puente, Mathematical Reviews, December, 2013)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.
| SKU | Unavailable |
| ISBN 13 | 9781461437185 |
| ISBN 10 | 1461437180 |
| Title | Markov Bases in Algebraic Statistics |
| Author | Satoshi Aoki |
| Series | Springer Series In Statistics |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer-Verlag New York Inc. |
| Year published | 2012-07-24 |
| Number of pages | 300 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |