Statistical Inference and Machine Learning for Big Data
Series: Springer Series in the Data Sciences;
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63 540 Ft
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Product details:
- Edition number 1st ed. 2022
- Publisher Springer
- Date of Publication 1 December 2022
- Number of Volumes 1 pieces, Book
- ISBN 9783031067839
- Binding Hardback
- No. of pages431 pages
- Size 279x210 mm
- Weight 1357 g
- Language English
- Illustrations 27 Illustrations, black & white; 66 Illustrations, color 467
Categories
Short description:
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.
Long description:
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.
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Table of Contents:
I. Introduction to Big Data.- Examples of Big Data.- II. Statistical Inference for Big Data.- Basic Concepts in Probability.- Basic Concepts in Statistics.- Multivariate Methods.- Nonparametric Statistics.- Exponential Tilting and its Applications.- Counting Data Analysis.- Time Series Methods.- Estimating Equations.- Symbolic Data Analysis.- III Machine Learning for Big Data.- Tools for Machine Learning.- Neural Networks.- IV Computational Methods for Statistical Inference.- Bayesian Computation Methods.
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Statistical Inference and Machine Learning for Big Data
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