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010 a| 2022060269;

020 a| 9780367183738q| (hardback);

020 a| 9781032481470q| (paperback);

020 z| 9780429061189q| (ebook);

035 a| (WaSeSS)ssj0002847557;

040 a| LBSORb| engc| LBSORd| WaSeSS;

042 a| pcc;

049 a| EREEa| NEHH;

050 00 a| QA279b| .L519 2023;

082 00 a| 519.5/382| 23/eng/20230609;

100 1 a| Liang, F.q| (Faming),d| 1970-?| UNAUTHORIZED;

245 10 a| Sparse graphical modeling for high dimensional datah| [electronic resource] :b| a paradigm of conditional independence tests /c| Faming Liang, Bochao Jia.;

260 a| Boca Raton :b| CRC Press,c| 2023.;

263 a| 2307;

300 a| pages cm;

490 0 a| Chapman & Hall/CRC monographs on statistics & applied probability;

504 a| Includes bibliographical references and index.;

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.;

506 a| Available only to authorized users.;

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.;

538 a| Mode of access: World Wide Web;

650 0 a| Graphical modeling (Statistics)=| ^A379569;

655 0 a| Electronic books.=| ^A491897;

700 1 a| Jia, Bochao.?| UNAUTHORIZED;

856 40 z| Full text available from eBooks on EBSCOhostu| https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=3618357&scope=site&custid=s5822723&authtype=ip,shib;

949 a| CLICK ON WEB ADDRESSw| ASISh| JOYNER188;

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999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6785958-2001l| HSLELECm| HSLr| Ys| Yt| HEBKu| 7/20/2026x| EBOOKz| HERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 6785958-3001l| MNETm| JMUSICr| Ys| Yt| MNESSBKu| 7/20/2026x| EBOOKz| JERESOURCE;

Sparse graphical modeling for high dimensional data a paradigm of conditional independence tests / Faming Liang, Bochao Jia.

Author/creator	Liang, F., 1970-
Other author	Jia, Bochao.
Format	Electronic
Publication Info	Boca Raton : CRC Press, 2023.
Description	pages cm
Supplemental Content	Full text available from eBooks on EBSCOhost
Subjects	Graphical modeling (Statistics)

Series	Chapman & Hall/CRC monographs on statistics & applied probability
Contents	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.
Abstract	"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"-- Provided by publisher.
Bibliography note	Includes bibliographical references and index.
Access restriction	Available only to authorized users.
Technical details	Mode of access: World Wide Web
Genre/form	Electronic books.
LCCN	2022060269
ISBN	9780367183738 (hardback)
ISBN	9781032481470 (paperback)
ISBN	(ebook)

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Sparse graphical modeling for high dimensional data a paradigm of conditional independence tests / Faming Liang, Bochao Jia.

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