國立虎尾科技大學 |

Statistical Inference and Machine Learning for Big Data

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Statistical Inference and Machine Learning for Big Data/ by Mayer Alvo.
作者:	Alvo, Mayer.
面頁冊數:	XXIV, 431 p. 93 illus., 66 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Data Science. -
電子資源:	https://doi.org/10.1007/978-3-031-06784-6
ISBN:	9783031067846

Statistical Inference and Machine Learning for Big Data
Alvo, Mayer.

Statistical Inference and Machine Learning for Big Data[electronic resource] /by Mayer Alvo. - 1st ed. 2022. - XXIV, 431 p. 93 illus., 66 illus. in color.online resource. - 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

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

1174436
Data Science.

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Statistical Inference and Machine Learning for Big Data
LDR:03029nam a22004095i 4500 001 1086017
003 DE-He213
005 20221130173024.0
007 cr nn 008mamaa
008 221228s2022 sz | s |||| 0|eng d
020 $a 9783031067846 $9 978-3-031-06784-6
024 7 $a 10.1007/978-3-031-06784-6 $2 doi
035 $a 978-3-031-06784-6
050 4 $a QA276-280
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
100 1 $a Alvo, Mayer. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1210565
245 1 0 $a Statistical Inference and Machine Learning for Big Data $h [electronic resource] / $c by Mayer Alvo.
250 $a 1st ed. 2022.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a XXIV, 431 p. 93 illus., 66 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Springer Series in the Data Sciences, $x 2365-5682
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.
650 2 4 $a Data Science. $3 1174436
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Statistics. $3 556824
650 1 4 $a Mathematical Statistics. $3 1366363
650 0 $a Artificial intelligence—Data processing. $3 1366684
650 0 $a Machine learning. $3 561253
650 0 $a Statistics . $3 1253516
650 0 $a Mathematical statistics. $3 527941
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783031067839
776 0 8 $i Printed edition: $z 9783031067853
776 0 8 $i Printed edition: $z 9783031067860
830 0 $a Springer Series in the Data Sciences, $x 2365-5674 $3 1265148
856 4 0 $u https://doi.org/10.1007/978-3-031-06784-6
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)