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Sparse graphical modeling for high dimensional data :a paradigm of conditional independence tests /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Sparse graphical modeling for high dimensional data :Faming Liang and Bochao Jia.
其他題名:	a paradigm of conditional independence tests /
作者:	Liang, F.
其他作者:	Jia, Bochao,
面頁冊數:	1 online resource (xix, 129 pages) :illustrations.
標題:	Graphical modeling (Statistics)
電子資源:	https://www.taylorfrancis.com/books/9780429061189
電子資源:	https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9780429584800
ISBN:	9780429061189$qelectronic book

Sparse graphical modeling for high dimensional data :a paradigm of conditional independence tests /
Liang, F.1970-

Sparse graphical modeling for high dimensional data :a paradigm of conditional independence tests /Faming Liang and Bochao Jia. - 1 online resource (xix, 129 pages) :illustrations. - Monographs on statistics and applied probability172. - Monographs on statistics and applied probability (Series) ;119..

Includes bibliographical references and index.

Introduction to sparse graphical models -- Gaussian graphical models -- Gaussian graphical modeling with missing data -- Gaussian graphical modeling for heterogeneous data -- Poisson graphical models -- Mixed graphical models -- Joint estimation of multiple graphical models -- Nonlinear and non-Gaussian graphical models -- High-dimensional inference with the aid of sparse graphical modeling.

"This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines"--

ISBN: 9780429061189$qelectronic book

Standard No.: 10.1201/9780429061189doi

Source: 9780429582905Ingram Content Group

LCCN: 2022060270

Nat. Bib. No.: GBC3B1167bnb

Nat. Bib. Agency Control No.: 021089804UkSubjects--Topical Terms:

191008
Graphical modeling (Statistics)

LC Class. No.: QA279 / .L519 2023

Dewey Class. No.: 519.5/38

Sparse graphical modeling for high dimensional data :a paradigm of conditional independence tests /
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245 1 0 $a Sparse graphical modeling for high dimensional data : $b a paradigm of conditional independence tests / $c Faming Liang and Bochao Jia.
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300 $a 1 online resource (xix, 129 pages) : $b illustrations.
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505 0 $a Introduction to sparse graphical models -- Gaussian graphical models -- Gaussian graphical modeling with missing data -- Gaussian graphical modeling for heterogeneous data -- Poisson graphical models -- Mixed graphical models -- Joint estimation of multiple graphical models -- Nonlinear and non-Gaussian graphical models -- High-dimensional inference with the aid of sparse graphical modeling.
520 $a "This book provides a general framework for learning sparse graphical models with conditional independence tests. It includes complete treatments for Gaussian, Poisson, multinomial, and mixed data; unified treatments for covariate adjustments, data integration, and network comparison; unified treatments for missing data and heterogeneous data; efficient methods for joint estimation of multiple graphical models; effective methods of high-dimensional variable selection; and effective methods of high-dimensional inference. The methods possess an embarrassingly parallel structure in performing conditional independence tests, and the computation can be significantly accelerated by running in parallel on a multi-core computer or a parallel architecture. This book is intended to serve researchers and scientists interested in high-dimensional statistics, and graduate students in broad data science disciplines"-- $c Provided by publisher.
588 $a Description based on online resource; title from digital title page (viewed on July 31, 2023).
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