Open Daily: 10am - 10pm | Alley door pickup 10am - 7pm 3038 Hennepin Ave Minneapolis, MN
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  • Open Daily: 10am - 10pm
    Alley-side Pickup: 10am - 7pm

    3038 Hennepin Ave Minneapolis, MN
    612-822-4611

Open Daily: 10am - 10pm | Alley-side Pickup: 10am - 7pm
3038 Hennepin Ave Minneapolis, MN
612-822-4611
Sparse Graphical Modeling for High Dimensional Data: A Paradigm of Conditional Independence Tests

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

Liang, Faming
Jia, Bochao

Hardcover

Series: Chapman & Hall/CRC Monographs on Statistics and Applied Prob

DatabasesProbability & Statistics

ISBN10: 0367183730
ISBN13: 9780367183738
Publisher: CRC Press
Published: Aug 2 2023
Pages: 130
Weight: 0.86
Height: 0.38 Width: 6.14 Depth: 9.21
Language: English

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.

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