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Partitions, hpergeometric sstems, an...

Mano, Shuhei.

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Partitions, hpergeometric sstems, and drichlet processes in statistics

Record Type:	Electronic resources : Monograph/item
Title/Author:	Partitions, hpergeometric sstems, and drichlet processes in statistics/ by Shuhei Mano.
Author:	Mano, Shuhei.
Published:	Tokyo :Springer Japan : : 2018.,
Description:	viii, 135 p. :digital ;24 cm.
[NT 15003449]:	Chapter 1. Introduction -- Chapter 2. Random combinatorial models -- Chapter 3. Statistical inferences -- Chapter 4. Examples.
Contained By:	Springer eBooks
Subject:	Mathematical statistics. -
Online resource:	http://dx.doi.org/10.1007/978-4-431-55888-0
ISBN:	9784431558880

Partitions, hpergeometric sstems, and drichlet processes in statistics
Mano, Shuhei.

Partitions, hpergeometric sstems, and drichlet processes in statistics[electronic resource] /by Shuhei Mano. - Tokyo :Springer Japan :2018. - viii, 135 p. :digital ;24 cm. - SpringerBriefs in statistics,2191-544X. - SpringerBriefs in statistics..

Chapter 1. Introduction -- Chapter 2. Random combinatorial models -- Chapter 3. Statistical inferences -- Chapter 4. Examples.

This book focuses on statistical inferences related to various combinatorial stochastic processes. Specifically, it discusses the intersection of three subjects that are generally studied independently of each other: partitions, hypergeometric systems, and Dirichlet processes. The Gibbs partition is a family of measures on integer partition, and several prior processes, such as the Dirichlet process, naturally appear in connection with infinite exchangeable Gibbs partitions. Examples include the distribution on a contingency table with fixed marginal sums and the conditional distribution of Gibbs partition given the length. The A-hypergeometric distribution is a class of discrete exponential families and appears as the conditional distribution of a multinomial sample from log-affine models. The normalizing constant is the A-hypergeometric polynomial, which is a solution of a system of linear differential equations of multiple variables determined by a matrix A, called A-hypergeometric system. The book presents inference methods based on the algebraic nature of the A-hypergeometric system, and introduces the holonomic gradient methods, which numerically solve holonomic systems without combinatorial enumeration, to compute the normalizing constant. Furher, it discusses Markov chain Monte Carlo and direct samplers from A-hypergeometric distribution, as well as the maximum likelihood estimation of the A-hypergeometric distribution of two-row matrix using properties of polytopes and information geometry. The topics discussed are simple problems, but the interdisciplinary approach of this book appeals to a wide audience with an interest in statistical inference on combinatorial stochastic processes, including statisticians who are developing statistical theories and methodologies, mathematicians wanting to discover applications of their theoretical results, and researchers working in various fields of data sciences.

ISBN: 9784431558880

Standard No.: 10.1007/978-4-431-55888-0doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276 / .M366 2018

Dewey Class. No.: 519.5

Partitions, hpergeometric sstems, and drichlet processes in statistics
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520 $a This book focuses on statistical inferences related to various combinatorial stochastic processes. Specifically, it discusses the intersection of three subjects that are generally studied independently of each other: partitions, hypergeometric systems, and Dirichlet processes. The Gibbs partition is a family of measures on integer partition, and several prior processes, such as the Dirichlet process, naturally appear in connection with infinite exchangeable Gibbs partitions. Examples include the distribution on a contingency table with fixed marginal sums and the conditional distribution of Gibbs partition given the length. The A-hypergeometric distribution is a class of discrete exponential families and appears as the conditional distribution of a multinomial sample from log-affine models. The normalizing constant is the A-hypergeometric polynomial, which is a solution of a system of linear differential equations of multiple variables determined by a matrix A, called A-hypergeometric system. The book presents inference methods based on the algebraic nature of the A-hypergeometric system, and introduces the holonomic gradient methods, which numerically solve holonomic systems without combinatorial enumeration, to compute the normalizing constant. Furher, it discusses Markov chain Monte Carlo and direct samplers from A-hypergeometric distribution, as well as the maximum likelihood estimation of the A-hypergeometric distribution of two-row matrix using properties of polytopes and information geometry. The topics discussed are simple problems, but the interdisciplinary approach of this book appeals to a wide audience with an interest in statistical inference on combinatorial stochastic processes, including statisticians who are developing statistical theories and methodologies, mathematicians wanting to discover applications of their theoretical results, and researchers working in various fields of data sciences.
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