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About Sparse Graphical Modeling for High Dimensional Data (Chapman and Hall CRC Monographs on Statistics and Applied Probability)
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
Detailed Information
| Author: | Faming Liang |
|---|---|
| Publication Year: | 2023 |
| ISBN: | 9780367183738 |
| Pages: | 151 |
| Language: | English |
| File Size: | 9.4 |
| Format: | |
| Price: | FREE |
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