Posted by **DZ123** at Feb. 1, 2017

English | 2005 | ISBN: 0849331579 | PDF | pages: 320 | 3.0 mb

Posted by **ChrisRedfield** at Dec. 13, 2015

Published: 2012-12-05 | ISBN: 3642359795 | PDF | 217 pages | 4.27 MB

Posted by **ChrisRedfield** at Dec. 1, 2015

Published: 2013-10-16 | ISBN: 3319016458 | PDF | 350 pages | 3.38 MB

Posted by **nebulae** at April 24, 2015

English | ISBN: 331915656X | 2015 | 616 pages | PDF | 8 MB

Posted by **step778** at March 3, 2015

2009 | pages: 329 | ISBN: 1596934069 | PDF | 6,5 mb

Posted by **tanas.olesya** at Dec. 13, 2014

English | January 31, 2002 | ISBN: 1402000200 | 342 pages | PDF | 19 MB

The advent of very large scale integrated circuit technology has enabled the construction of very complex and large interconnection networks. By most accounts, the next generation of supercomputers will achieve its gains by increasing the number of processing elements, rather than by using faster processors.

Posted by **tanas.olesya** at Nov. 7, 2014

Cambridge University Press | April 3, 2006 | English | ISBN: 0521855152 | 543 pages | PDF | 10 MB

This rigorous and self-contained book describes mathematical and, in particular, stochastic methods to assess the performance of networked systems. It consists of three parts. Part one is a review of probability theory. Part two covers the classical theory of stochastic processes (Poisson, renewal, Markov, and queuing theory), which are considered to be the basic building blocks for performance evaluation studies.

Posted by **fdts** at Oct. 25, 2014

by Matthias Dehmer and Frank Emmert-Streib

English | 2009 | ISBN: 3527323457 | 480 pages | PDF | 4.2 MB

Posted by **ChrisRedfield** at Sept. 30, 2014

Published: 2013-12-03 | ISBN: 3319019759 | PDF | 183 pages | 5 MB

Posted by **Decisive** at Sept. 25, 2014

Publisher: Imperial College Press | ISBN: 1848164335 | edition 2009 | PDF | 179 pages | 3,07 mb

Networks provide a very useful way to describe a wide range of different data types in biology, physics and elsewhere. Apart from providing a convenient tool to visualize highly dependent data, networks allow stringent mathematical and statistical analysis. In recent years, much progress has been achieved to interpret various types of biological network data such as transcriptomic, metabolomic and protein interaction data as well as epidemiological data.