Modeling with Data: Tools and Techniques for Scientific Computing
by Ben Klemens
Publisher: Princeton University Press 2009
Number of pages: 470
The book fully explains how to execute computationally intensive analysis on very large data sets, showing readers how to determine the best methods for solving a variety of different problems, how to create and debug statistical models, and how to run an analysis and evaluate the results. Ben Klemens introduces a set of open and unlimited tools, and uses them to demonstrate data management, analysis, and simulation techniques essential for dealing with large data sets and computationally intensive procedures. Modeling with Data will interest anyone looking for a comprehensive guide to these powerful statistical tools, including researchers and graduate students in the social sciences, biology, engineering, economics, and applied mathematics.
Download or read it online for free here:
by F. Baccelli, G. Cohen, G. J. Olsder, J. Quadrat - John Wiley & Sons
Presents new modelling and analysis techniques for the description of discrete event dynamic systems. Created within the text is a calculus which allows the derivation of analytical tools for computing the time behavior of this type of system.
by Norm Matloff - University of California, Davis
The materials here form a textbook for a course in mathematical probability and statistics for computer science students. Computer science examples are used throughout, in areas such as: computer networks; data and text mining; computer security...
by Giuseppe Petrone, Giuliano Cammarata - InTech
This book collects research studies concerning modeling and simulation of physical systems in a very wide range of applications: micro-electro-mechanical systems, measurement instrumentations, catalytic reactors, biomechanical applications, etc.
by Harry Perros - NC State University
The generation of pseudo-random numbers, the generation of stochastic variates, simulation designs, estimation techniques for analyzing endogenously created data, validation of a simulation model, variance reduction techniques, etc.