Overview
- Introduces a comprehensive collection of topics in modern statistical areas
- Presents applications to topics in genetics and environmental science
- Suitable for upper undergraduate and graduate students as well as researchers
Part of the book series: Springer Series in the Data Sciences (SSDS)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (13 chapters)
-
Front Matter
-
Part I
-
Front Matter
-
-
Machine Learning for Big Data
-
Front Matter
-
-
Computational Methods for Statistical Inference
-
Front Matter
-
-
Back Matter
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Statistical Inference and Machine Learning for Big Data
Authors: Mayer Alvo
Series Title: Springer Series in the Data Sciences
DOI: https://doi.org/10.1007/978-3-031-06784-6
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-031-06783-9Published: 01 December 2022
Softcover ISBN: 978-3-031-06786-0Published: 01 December 2023
eBook ISBN: 978-3-031-06784-6Published: 30 November 2022
Series ISSN: 2365-5674
Series E-ISSN: 2365-5682
Edition Number: 1
Number of Pages: XXIV, 431
Number of Illustrations: 27 b/w illustrations, 66 illustrations in colour
Topics: Probability Theory and Stochastic Processes, Applications of Mathematics, Statistical Theory and Methods, Statistics, general, Machine Learning, Data Structures and Information Theory