UID:
almahu_9949568088702882
Format:
1 online resource (xix, 129 pages) :
,
illustrations.
ISBN:
9780429061189
,
0429061188
,
9780429584800
,
0429584806
,
9780429582905
,
0429582900
Series Statement:
Monographs on statistics and applied probability 172
Content:
"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"--
Note:
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.
Additional Edition:
Print version: Liang, F. 1970- Sparse graphical modeling for high dimensional data Boca Raton : CRC Press, 2023 ISBN 9780367183738
Language:
English
DOI:
10.1201/9780429061189
URL:
https://www.taylorfrancis.com/books/9780429061189
