Statistical Inference and Machine Learning for Big Data

Mayer Alvo

ISBN 10: 3031067835 / ISBN 13: 9783031067839

Published by Springer International Publishing, 2022

About this Item

Druck auf Anfrage Neuware - Printed after ordering - 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.

Seller Inventory # 9783031067839

Bibliographic Details

• Title: Statistical Inference and Machine Learning for Big Data
• Publisher: Springer International Publishing
• Publication Date: 2022
• Language: English
• ISBN 10: 3031067835
• ISBN 13: 9783031067839
• Binding: Buch
• Condition: Neu
• Dimensions: 285W x 215H x 30D millimeters 285 millimeters width by 215 millimeters height by 30 millimeters depth
• Weight: 1357 grams

About this title

Synopsis

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.