Sparse Graphical Modeling for High Dimensional Data
A Paradigm of Conditional Independence Tests
- 149 pages
- English
- ePUB (mobile friendly)
- Available on iOS & Android
Sparse Graphical Modeling for High Dimensional Data
A Paradigm of Conditional Independence Tests
About this book
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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Information
Table of contents
- Cover
- Half Title Page
- Series Page
- Title Page
- Copyright Page
- Dedication
- Table of Contents
- Preface
- Authors
- List of Figures
- List of Tables
- List of Symbols
- 1 Introduction to Sparse Graphical Models
- 2 Gaussian Graphical Models
- 3 Gaussian Graphical Modeling with Missing Data
- 4 Gaussian Graphical Modeling for Heterogeneous Data
- 5 Poisson Graphical Models
- 6 Mixed Graphical Models
- 7 Joint Estimation of Multiple Graphical Models
- 8 Nonlinear and Non-Gaussian Graphical Models
- 9 High-Dimensional Inference with the Aid of Sparse Graphical Modeling
- Appendix
- Bibliography
- Index