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- Gratis thuislevering in België vanaf € 30
- Ruim aanbod met 7 miljoen producten
Sparse Graphical Modeling for High Dimensional Data
A Paradigm of Conditional Independence Tests
Faming Liang, Bochao Jia
€ 193,95
+ 387 punten
Omschrijving
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
Specificaties
Betrokkenen
- Auteur(s):
- Uitgeverij:
Inhoud
- Aantal bladzijden:
- 130
- Taal:
- Engels
- Reeks:
Eigenschappen
- Productcode (EAN):
- 9780367183738
- Verschijningsdatum:
- 02/08/2023
- Uitvoering:
- Hardcover
- Formaat:
- Genaaid
- Afmetingen:
- 156 mm x 234 mm
- Gewicht:
- 390 g
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