الصفحة 1
الصفحة 1
Numerical Methods Using Java : For Data Science, Analysis, and Engineering
Covers a wide range of topics, including chapters on linear algebra, root finding, curve fitting, differentiation and integration, solving differential equations, random numbers and simulation, a whole suite of unconstrained and constrained optimization algorithms, statistics, regression and time series analysis. The mathematical concepts behind the algorithms are clearly explained, with plenty of code examples and illustrations to help even beginners get started. You will: Program in Java using a high-performance numerical library / Learn the mathematics for a wide range of numerical computing algorithms / Convert ideas and equations into code / Put together algorithms/ and classes to build your own engineering solution / Build solvers for industrial optimization problems / Do data analysis using basic and advanced statistics
Nexus Network Journal 9,1 : Architecture and Mathematics
This issue is dedicated to various kinds of patterns in architecture. Buthayna Eilouti and Amer Al-Jokhadar address patterns in shape grammars in the ground plans of Mamluk madrasas, religious schools. Giulio Magli goes back further in history, to the age of Greek colonies in Italy before they were conquered by the Romans, to examine patterns in urban design. In Traditional Patterns in Pyrgi of Chios: Mathematics and Community Charoula Stathopoulou examines the geometric patterns that decorate the buildings of the town of Pyrgi, on the Greek island of Chios. Curve Fitting is a study of ways to construct a function so that its graph most closely approximates the pattern given by a set of points.
Introductory Statistics with R
R is an Open Source implementation of the S language. It works on multiple computing platforms and can be freely downloaded. R is now in widespread use for teaching at many levels as well as for practical data analysis and methodological development. This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. A supplementary R package can be downloaded and contains the data sets.
Data Analysis Using the Method of Least Squares : Extracting the Most Information from Experiments
The preferred method of data analysis of quantitative experiments is the method of least squares. Often, however, the full power of the method is overlooked and very few books deal with this subject at the level that it deserves. The purpose of Data Analysis Using the Methods of Least Squares is to fill this gap and include the type of information required to help scientists and engineers apply the method to problems in their special fields of interest.Linear and non-linear least squares, the use of experimental error estimates for data weighting, procedures to include prior estimates, methodology for selecting and testing models, prediction analysis, and some non-parametric methods are discussed.
Advances in Mass Data Analysis of Images and Signals in Medicine, Biotechnology, Chemistry and Food Industry ; 3rd International Conference, MDA 2008 Leipzig, Germany, July 14, 2008 Proceedings
Presents the broad and growing scientific evidence linking mass data analysis with challenging problems in medicine, biotechnology and chemistry.
