Numeric Computation and Statistical Data Analysis on the Java Platform

Numeric Computation and Statistical Data Analysis on the Java Platform
Author :
Publisher : Springer
Total Pages : 620
Release :
ISBN-10 : 9783319285313
ISBN-13 : 3319285319
Rating : 4/5 (319 Downloads)

Book Synopsis Numeric Computation and Statistical Data Analysis on the Java Platform by : Sergei V. Chekanov

Download or read book Numeric Computation and Statistical Data Analysis on the Java Platform written by Sergei V. Chekanov and published by Springer. This book was released on 2016-03-23 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of this book. The Python code examples powered by the Java platform can easily be transformed to other programming languages, such as Java, Groovy, Ruby and BeanShell. This book equips the reader with a computational platform which, unlike other statistical programs, is not limited by a single programming language. The author focuses on practical programming aspects and covers a broad range of topics, from basic introduction to the Python language on the Java platform (Jython), to descriptive statistics, symbolic calculations, neural networks, non-linear regression analysis and many other data-mining topics. He discusses how to find regularities in real-world data, how to classify data, and how to process data for knowledge discoveries. The code snippets are so short that they easily fit into single pages. Numeric Computation and Statistical Data Analysis on the Java Platform is a great choice for those who want to learn how statistical data analysis can be done using popular programming languages, who want to integrate data analysis algorithms in full-scale applications, and deploy such calculations on the web pages or computational servers regardless of their operating system. It is an excellent reference for scientific computations to solve real-world problems using a comprehensive stack of open-source Java libraries included in the DataMelt (DMelt) project and will be appreciated by many data-analysis scientists, engineers and students.


Numeric Computation and Statistical Data Analysis on the Java Platform Related Books

Numeric Computation and Statistical Data Analysis on the Java Platform
Language: en
Pages: 620
Authors: Sergei V. Chekanov
Categories: Computers
Type: BOOK - Published: 2016-03-23 - Publisher: Springer

DOWNLOAD EBOOK

Numerical computation, knowledge discovery and statistical data analysis integrated with powerful 2D and 3D graphics for visualization are the key topics of thi
Software for Data Analysis
Language: en
Pages: 515
Authors: John Chambers
Categories: Computers
Type: BOOK - Published: 2008-06-14 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

John Chambers turns his attention to R, the enormously successful open-source system based on the S language. His book guides the reader through programming wit
Elements of Statistical Computing
Language: en
Pages: 297
Authors: R.A. Thisted
Categories: Mathematics
Type: BOOK - Published: 2017-10-19 - Publisher: Routledge

DOWNLOAD EBOOK

Statistics and computing share many close relationships. Computing now permeates every aspect of statistics, from pure description to the development of statist
Computational Methods for Data Analysis
Language: en
Pages: 302
Authors: John M. Chambers
Categories: Mathematical statistics
Type: BOOK - Published: 1977 - Publisher: John Wiley & Sons

DOWNLOAD EBOOK

Programming; Data management and manipulation; Numerical computations; Linear models; Nonlinear models; Simulation of Random processes; Computational graphics.
Computational Statistics
Language: en
Pages: 732
Authors:
Categories: Mathematics
Type: BOOK - Published: 2010-04-29 - Publisher: Springer Science & Business Media

DOWNLOAD EBOOK

Computational inference is based on an approach to statistical methods that uses modern computational power to simulate distributional properties of estimators