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Více informací o knize Sparse Graphical Modeling for High Dimensional Data

• Parametry knihy
• Anotace
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Anotace knihy

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.

Key Features:

• A general framework for learning sparse graphical models with conditional independence tests








• Complete treatments for different types of data, Gaussian, Poisson, multinomial, and mixed data








• Unified treatments for data integration, network comparison, and covariate adjustment








• Unified treatments for missing data and heterogeneous data








• Efficient methods for joint estimation of multiple graphical models








• Effective methods of high-dimensional variable selection




• Effective methods of high-dimensional inference

Parametry knihy

• Plný název: Sparse Graphical Modeling for High Dimensional Data
• Podnázev: A Paradigm of Conditional Independence Tests
• Autor: Faming Liang, Jia, Bochao (Eli Lilly and Company, Corporate Center, Indianapolis IN 46285)
• Jazyk: Angličtina
• Vazba: Pevná
• Počet stran: 149
• EAN: 9780367183738
• ID: 43238485
• Nakladatelství: Taylor & Francis Ltd
• Hmotnost: 348 g
• Rozměry: 234 × 156 mm
• Datum vydání: 02. August 2023

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