Statistical Inference and Machine Learning for Big Data

Mayer Alvo

ISBN 10: 3031067835 / ISBN 13: 9783031067839

Editore: Springer International Publishing Dez 2022, 2022

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This item is printed on demand - it takes 3-4 days longer - Neuware -This book presents a variety ofadvanced statistical methods ata level suitable foradvanced undergraduate and graduate students as well as for others interested in familiarizing themselves withthese important subjects. It proceeds toillustrate these methods in thecontext ofreal-life applications in a variety of areas such as genetics, medicine, and environmental problems.Thebook begins in Part I byoutlining various data types and byindicating how these are normally represented graphically and subsequently analyzed. In Part II, thebasic tools in probability and statistics are introduced with special reference to symbolic data analysis. Themost useful and relevant results pertinent tothis 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 forstatistical methods attheundergraduate and graduate level as well as be useful in courses which aim toprovide an overview ofmodern statistics and its applications. 456 pp. Englisch.

Codice articolo 9783031067839

Dati bibliografici

• Titolo: Statistical Inference and Machine Learning for Big Data
• Editore: Springer International Publishing Dez 2022
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 3031067835
• ISBN 13: 9783031067839
• Rilegatura: Buch
• Condizione: Neu
• Dimensioni: 285 L x 215 A x 30 P (millimetri) 285 millimetri di larghezza per 215 millimetri di altezza per 30 millimetri di profondità
• Peso: 1357 grammi

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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.