Sparse graphical modeling for high dimensional data : a paradigm of conditional independence tests 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