Sparse Graphical Modeling for High Dimensional Data

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-dim...See More