國立高雄大學圖資館 |

Language: English

Back

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

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Sparse graphical modeling for high dimensional data :Faming Liang and Bochao Jia.
Reminder of title:	a paradigm of conditional independence tests /
Author:	Liang, F.
other author:	Jia, Bochao,
Description:	1 online resource (xix, 129 pages) :illustrations.
Subject:	Graphical modeling (Statistics)
Online resource:	https://www.taylorfrancis.com/books/9780429061189
Online resource:	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 /
LDR:03771cam 2200625 i 4500 001 655142
003 OCoLC
005 20240606103730.0
006 m o d
007 cr cnu---unuuu
008 240606t20232023flua ob 001 0 eng
010 $a 2022060270
015 $a GBC3B1167 $2 bnb
016 7 $a 021089804 $2 Uk
019 $a 1384498386
020 $a 9780429061189$qelectronic book
020 $a 0429061188$qelectronic book
020 $a 9780429584800$qelectronic book
020 $a 0429584806$qelectronic book
020 $a 9780429582905$qelectronic book
020 $a 0429582900$qelectronic book
020 $z 9780367183738$qhardcover
020 $z 0367183730$qhardcover
020 $z 9781032481470$qpaperback
024 7 $a 10.1201/9780429061189 $2 doi
029 1 $a UKMGB $b 021089804
029 1 $a AU@ $b 000074485041
035 $a (OCoLC)1380685859 $z (OCoLC)1384498386
035 $a on1380685859
037 $a 9780429582905 $b Ingram Content Group
040 $a DLC $b eng $e rda $c DLC $d YDX $d UKMGB $d TYFRS $d OCLCF $d YDX $d OCLCO $d UKAHL
042 $a pcc
049 $a NUKM
050 0 4 $a QA279 $b .L519 2023
072 7 $a MAT $x 029000 $2 bisacsh
072 7 $a PBT $2 bicssc
082 0 0 $a 519.5/38 $2 23/eng/20230609
100 1 $a Liang, F. $q (Faming), $d 1970- $3 452555
245 1 0 $a Sparse graphical modeling for high dimensional data : $b a paradigm of conditional independence tests / $c Faming Liang and Bochao Jia.
264 1 $a Boca Raton, FL : $b CRC Press, $c 2023
264 4 $c ©2023
300 $a 1 online resource (xix, 129 pages) : $b illustrations.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
490 1 $a Monographs on statistics and applied probability $v 172
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.
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).
650 0 $a Graphical modeling (Statistics) $3 191008
650 6 $a Modéles graphiques (Statistique) $3 966386
650 7 $a MATHEMATICS / Probability & Statistics / General $2 bisacsh $3 966309
700 1 $a Jia, Bochao, $e author. $3 966310
776 0 8 $i Print version: $a Liang, F. 1970- $t Sparse graphical modeling for high dimensional data $d Boca Raton : CRC Press, 2023 $z 9780367183738 $w (DLC) 2022060269
830 0 $a Monographs on statistics and applied probability (Series) ; $v 119. $3 649485
856 4 0 $3 Taylor & Francis $u https://www.taylorfrancis.com/books/9780429061189
856 4 0 $u https://www.vlebooks.com/vleweb/product/openreader?id=none&isbn=9780429584800
938 $a Askews and Holts Library Services $b ASKH $n AH41071364
938 $a YBP Library Services $b YANK $n 18617542
938 $a YBP Library Services $b YANK $n 20222704
994 $a C0 $b TWNUK