Sparse Graphical Modeling for High Dimensional Data

A Paradigm of Conditional Independence Tests

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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.