Velký výběr
Nabízíme miliony knih v angličtině. Od beletrie až po ty nejodborněji odborné.
ISBN | 9780367183738 |
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Autor | Liang Faming |
Vydavatel | Crc Pr Inc |
Jazyk | english |
Vazba | Pevná vazba |
Rok vydání | 2023 |
Počet stran | 130 |
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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