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Computational and Statistical Methods for Analysing Big Data with Applications

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Release : 2015-11-20
Genre : Mathematics
Kind : eBook
Book Rating : 519/5 ( reviews)

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Book Synopsis Computational and Statistical Methods for Analysing Big Data with Applications by : Shen Liu

Download or read book Computational and Statistical Methods for Analysing Big Data with Applications written by Shen Liu. This book was released on 2015-11-20. Available in PDF, EPUB and Kindle. Book excerpt: Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration. Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data. Advanced computational and statistical methodologies for analysing big data are developed Experimental design methodologies are described and implemented to make the analysis of big data more computationally tractable Case studies are discussed to demonstrate the implementation of the developed methods Five high-impact areas of application are studied: computer vision, geosciences, commerce, healthcare and transportation Computing code/programs are provided where appropriate

Handbook of Big Data Analytics

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Release : 2018-07-20
Genre : Computers
Kind : eBook
Book Rating : 846/5 ( reviews)

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Book Synopsis Handbook of Big Data Analytics by : Wolfgang Karl Härdle

Download or read book Handbook of Big Data Analytics written by Wolfgang Karl Härdle. This book was released on 2018-07-20. Available in PDF, EPUB and Kindle. Book excerpt: Addressing a broad range of big data analytics in cross-disciplinary applications, this essential handbook focuses on the statistical prospects offered by recent developments in this field. To do so, it covers statistical methods for high-dimensional problems, algorithmic designs, computation tools, analysis flows and the software-hardware co-designs that are needed to support insightful discoveries from big data. The book is primarily intended for statisticians, computer experts, engineers and application developers interested in using big data analytics with statistics. Readers should have a solid background in statistics and computer science.

Applications in Statistical Computing

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Release : 2019-10-12
Genre : Computers
Kind : eBook
Book Rating : 470/5 ( reviews)

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Book Synopsis Applications in Statistical Computing by : Nadja Bauer

Download or read book Applications in Statistical Computing written by Nadja Bauer. This book was released on 2019-10-12. Available in PDF, EPUB and Kindle. Book excerpt: This volume presents a selection of research papers on various topics at the interface of statistics and computer science. Emphasis is put on the practical applications of statistical methods in various disciplines, using machine learning and other computational methods. The book covers fields of research including the design of experiments, computational statistics, music data analysis, statistical process control, biometrics, industrial engineering, and econometrics. Gathering innovative, high-quality and scientifically relevant contributions, the volume was published in honor of Claus Weihs, Professor of Computational Statistics at TU Dortmund University, on the occasion of his 66th birthday.

Computational Methods for Data Analysis

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Release : 2018-12-17
Genre : Mathematics
Kind : eBook
Book Rating : 608/5 ( reviews)

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Book Synopsis Computational Methods for Data Analysis by : Yeliz Karaca

Download or read book Computational Methods for Data Analysis written by Yeliz Karaca. This book was released on 2018-12-17. Available in PDF, EPUB and Kindle. Book excerpt: This graduate text covers a variety of mathematical and statistical tools for the analysis of big data coming from biology, medicine and economics. Neural networks, Markov chains, tools from statistical physics and wavelet analysis are used to develop efficient computational algorithms, which are then used for the processing of real-life data using Matlab.

Data Analytics, Computational Statistics, and Operations Research for Engineers

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Release : 2022-03-24
Genre : Computers
Kind : eBook
Book Rating : 427/5 ( reviews)

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Book Synopsis Data Analytics, Computational Statistics, and Operations Research for Engineers by : Debabrata Samanta

Download or read book Data Analytics, Computational Statistics, and Operations Research for Engineers written by Debabrata Samanta. This book was released on 2022-03-24. Available in PDF, EPUB and Kindle. Book excerpt: With the rapidly advancing fields of Data Analytics and Computational Statistics, it’s important to keep up with current trends, methodologies, and applications. This book investigates the role of data mining in computational statistics for machine learning. It offers applications that can be used in various domains and examines the role of transformation functions in optimizing problem statements. Data Analytics, Computational Statistics, and Operations Research for Engineers: Methodologies and Applications presents applications of computationally intensive methods, inference techniques, and survival analysis models. It discusses how data mining extracts information and how machine learning improves the computational model based on the new information. Those interested in this reference work will include students, professionals, and researchers working in the areas of data mining, computational statistics, operations research, and machine learning.

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