In Print: ‘Sparse Graphical Modeling for High Dimensional Data’

Publication title

Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests

Purdue author

Faming Liang

Authors

Bochao Jia, Faming Liang

Publisher

Chapman & Hall

Publication date

August 2, 2023 

About the book (from the publisher)

This book offers a comprehensive framework for mastering the complexities of learning high-dimensional sparse graphical models through the use of conditional independence tests. These tests are strategically conducted within a Markov neighborhood, ensuring both the low dimensionality of the conditioning set and their equivalence to the original high-dimensional conditional independence tests. One notable feature of the methods outlined in this book is their inherent parallel structure for performing conditional independence tests. This enables significant computational acceleration when executed on a multi-core computer or a parallel architecture. This book is intended for researchers, scientists and graduate students in various data science disciplines.

About the Purdue author

Faming Liang is a Distinguished Professor of Statistics at Purdue University. He is an American Statistical Association fellow, an Institute of Mathematical Statistics fellow and an elected member of the International Statistical Institute. His research interests include machine learning, high-dimensional and big data statistics, Markov chain Monte Carlo, as well as interdisciplinary research in the fields of biomedical sciences and engineering. Currently, he serves as the co-editor for the Journal of Computational and Graphical Statistics.