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portada Sparse Graphical Modeling for High Dimensional Data (Chapman & Hall
Type
Physical Book
Publisher
Author
Language
English
Pages
150
Format
Hardcover
ISBN13
9780367183738
Edition No.
1

Sparse Graphical Modeling for High Dimensional Data (Chapman & Hall

Faming Liang; Bochao Jia (Author) · Crc Press, · Hardcover

Sparse Graphical Modeling for High Dimensional Data (Chapman & Hall - Faming Liang; Bochao Jia

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Synopsis "Sparse Graphical Modeling for High Dimensional Data (Chapman & Hall "

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.Key Features: A general framework for learning sparse graphical models with conditional independence tests Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data Unified treatments for data integration, network comparison, and covariate adjustment Unified treatments for missing data and heterogeneous data Efficient methods for joint estimation of multiple graphical models Effective methods of high-dimensional variable selection Effective methods of high-dimensional inference

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