This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. This book is valuable for students across disciplines, including students of computational sciences, statistics, and mathematics.
This book discusses the relevance of probabilistic supervised learning, to the pursuit of automated and reliable prediction of an unknown that is in a state of relationship with another variable. This book is valuable for students across disciplines, including students of computational sciences, statistics, and mathematics.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Dr. Dalia Chakrabarty is a Reader in Statistical Data Science in the Department of Mathematics at the University of York. Her PhD is from St. Cross College in the University of Oxford, and she works on the development of methods to permit the probabilistic learning of random variables of various kinds, given real world data that is diversely challenging.
Inhaltsangabe
Foreword Preface Acknowledgements 1. Inter variable relationships 2. Bayesianism 3. Supervised learning & prediction, using Gaussian Processes 4. Covariance kernels suitable for real world data 5. Learning a high dimensional function 6. A self assembled prior on correlation matrices Bibliography Index
Foreword Preface Acknowledgements 1. Inter variable relationships 2. Bayesianism 3. Supervised learning & prediction, using Gaussian Processes 4. Covariance kernels suitable for real world data 5. Learning a high dimensional function 6. A self assembled prior on correlation matrices Bibliography Index
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