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Statistical inference and machine learning for big data

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical inference and machine learning for big databy Mayer Alvo.
作者:	Alvo, Mayer.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xxiv, 431 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Big data.
電子資源:	https://doi.org/10.1007/978-3-031-06784-6
ISBN:	9783031067846$q(electronic bk.)

Statistical inference and machine learning for big data
Alvo, Mayer.

Statistical inference and machine learning for big data[electronic resource] /by Mayer Alvo. - Cham :Springer International Publishing :2022. - xxiv, 431 p. :ill., digital ;24 cm. - Springer series in the data sciences,2365-5682. - Springer series in the data sciences..

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.

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.

ISBN: 9783031067846$q(electronic bk.)

Standard No.: 10.1007/978-3-031-06784-6doiSubjects--Topical Terms:

609582
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Statistical inference and machine learning for big data
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505 0 $a 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.
520 $a 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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