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

Sparse Graphical Modeling for High Dimensional Data (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)

Computers & Technology

Liang, Faming

Jia, Bochao

Software

Chapman and Hall/CRC

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Books by Jia, Bochao

See the best price to sell, buy, or rent books by Jia, Bochao. BookScouter helps to compare book prices from 25+ online bookstores and 30+ buyback vendors with a single search.

Sparse Graphical Modeling for High Dimensional Data (Chapman & Hall/CRC Monographs on Statistics and Applied Probability)