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Patterns of Scalable Bayesian Inference - Foundations and Trends (R) in Machine Learning
Elaine Angelino
Patterns of Scalable Bayesian Inference - Foundations and Trends (R) in Machine Learning
Elaine Angelino
Identifies unifying principles, patterns, and intuitions for scaling Bayesian inference. This book examines how these techniques can be scaled up to larger problems and scaled out across parallel computational resources, and reviews existing work on utilizing computing resources with both MCMC and variational approximation techniques.
148 pages
Médias | Livres Paperback Book (Livre avec couverture souple et dos collé) |
Validé | 17 novembre 2016 |
ISBN13 | 9781680832181 |
Éditeurs | now publishers Inc |
Pages | 148 |
Dimensions | 156 × 234 × 8 mm · 217 g |
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