Statistical Inference and Machine Learning for Big Data (Hardcover)

Mayer Alvo

ISBN 10: 3031067835 / ISBN 13: 9783031067839

Editore: Springer International Publishing AG, Cham, 2022

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Hardcover. This book presents a variety of advanced statistical methods at a level suitable for advanced undergraduate and graduate students as well as for others interested in familiarizing themselves with these important subjects. It proceeds to illustrate these methods in the context of real-life applications in a variety of areas such as genetics, medicine, and environmental problems.The book begins in Part I by outlining various data types and by indicating how these are normally represented graphically and subsequently analyzed. In Part II, the basic tools in probability and statistics are introduced with special reference to symbolic data analysis. The most useful and relevant results pertinent to this book are retained. In Part III, the focus is on the tools of machine learning whereas in Part IV the computational aspects of BIG DATA are presented.This book would serve as a handy desk reference for statistical methods at the undergraduate and graduate level as well as be useful in courses which aim to provide an overview of modern statistics and its applications. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Codice articolo 9783031067839

Dati bibliografici

• Titolo: Statistical Inference and Machine Learning for Big Data (Hardcover)
• Editore: Springer International Publishing AG, Cham
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 3031067835
• ISBN 13: 9783031067839
• Rilegatura: Hardcover
• Condizione: new

Descrizione articolo

Riassunto

This book presents a variety of advanced statistical methods at a level suitable for advanced undergraduate and graduate students as well as for others interested in familiarizing themselves with these important subjects. It proceeds to illustrate these methods in the context of real-life applications in a variety of areas such as genetics, medicine, and environmental problems.

The book begins in Part I by outlining various data types and by indicating how these are normally represented graphically and subsequently analyzed. In Part II, the basic tools in probability and statistics are introduced with special reference to symbolic data analysis. The most useful and relevant results pertinent to this book are retained. In Part III, the focus is on the tools of machine learning whereas in Part IV the computational aspects of BIG DATA are presented.

This book would serve as a handy desk reference for statistical methods at the undergraduate and graduate level as well as be useful in courses which aim to provide an overview of modern statistics and its applications.