Analysis of Tcp Performance in Data Center

Analysis of TCP Performance in Data Center Networks (Repost)

Santosh Kulkarni and Prathima Agrawal, "Analysis of TCP Performance in Data Center Networks"
English | ISBN: 146147860X | 2014 | 112 pages | PDF | 3 MB

Analysis of TCP Performance in Data Center Networks  

Posted by nebulae at Oct. 7, 2013
Analysis of TCP Performance in Data Center Networks

Santosh Kulkarni and Prathima Agrawal, "Analysis of TCP Performance in Data Center Networks"
English | ISBN: 146147860X | 2014 | 112 pages | PDF | 3 MB

Analysis of Large and Complex Data  eBooks & eLearning

Posted by ksveta6 at Nov. 28, 2016
Analysis of Large and Complex Data

Analysis of Large and Complex Data (Studies in Classification, Data Analysis, and Knowledge Organization) by Adalbert F.X. Wilhelm, Hans A. Kestler
2016 | ISBN: 3319252240 | English | 656 pages | PDF | 16 MB

Analysis of Large and Complex Data  eBooks & eLearning

Posted by AlenMiler at Aug. 9, 2016
Analysis of Large and Complex Data

Analysis of Large and Complex Data (Studies in Classification, Data Analysis, and Knowledge Organization) by Adalbert F.X. Wilhelm
English | 24 Nov. 2016 | ISBN: 3319252240 | 684 Pages | EPUB (True) | 6.38 MB

This book offers a snapshot of the state-of-the-art in classification at the interface between statistics, computer science and application fields.
Advanced Analysis Of Gene Expression Microarray Data (repost)

Advanced Analysis Of Gene Expression Microarray Data by Aidong Zhang
English | 27 Jun. 2006 | ISBN: 071672619X | ASIN: 9812566457 | 356 Pages | PDF | 19 MB

This book focuses on the development and application of the latest advanced data mining, machine learning, and visualization techniques for the identification of interesting, significant, and novel patterns in gene expression microarray data. Biomedical researchers will find this book invaluable for learning the cutting-edge methods for analyzing gene expression microarray data.
Statistical Analysis of Next Generation Sequencing Data (Frontiers in Probability and the Statistical Sciences) (Repost)

Statistical Analysis of Next Generation Sequencing Data (Frontiers in Probability and the Statistical Sciences) By Somnath Datta, Dan Nettleton
2014 | 448 Pages | ISBN: 3319072110 | PDF | 8 MB
Analysis of Microarray Gene Expression Data (Trends in Logic) by Mei-Ling Ting Lee

Analysis of Microarray Gene Expression Data (Trends in Logic) by Mei-Ling Ting Lee
English | Apr 30, 2004 | ISBN: 0792370872 | 398 Pages | PDF | 15 MB

After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. Microarray technology provides a systematic way to survey DNA and RNA variation.
The Analysis of Cross-Classified Categorical Data by Stephen E. Fienberg

The Analysis of Cross-Classified Categorical Data by Stephen E. Fienberg
English | July 30, 2007 | ISBN: 0387728244 | 208 Pages | DJVU | 1 MB

This book presents some of the recent work on the statistical analysis of cross-classified data using longlinear models, especially in the multidimensional situation.

Statistical Analysis of Next Generation Sequencing Data  

Posted by interes at Oct. 28, 2014
Statistical Analysis of Next Generation Sequencing Data

Statistical Analysis of Next Generation Sequencing Data (Frontiers in Probability and the Statistical Sciences) by Somnath Datta and Dan Nettleton
English | 2014 | ISBN: 3319072110 | 432 pages | PDF | 8,3 MB

Next Generation Sequencing (NGS) is the latest high throughput technology to revolutionize genomic research. NGS generates massive genomic datasets that play a key role in the big data phenomenon that surrounds us today.
Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach by Gennady Andrienko [Repost]

Exploratory Analysis of Spatial and Temporal Data: A Systematic Approach by Gennady Andrienko
Springer; 2006 edition | December 21, 2005 | English | ISBN: 3540259945 | 712 pages | PDF | 15 MB

Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. As something relevant is detected in data, new questions arise, causing specific parts to be viewed in more detail. So EDA has a significant appeal: it involves hypothesis generation rather than mere hypothesis testing.