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Vector Generalized Linear and Additive Models With an Implementation in R
This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of statistical models, and fortified with necessary infrastructure to make the models more fully operable, the framework allows analyses based on many semi-traditional applied statistics models to be …
- Edition
- -
- ISBN/ISSN
- 978-1-4939-2818-7
- Collation
- -
- Series Title
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- Call Number
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Numerical Python
Numerical Python by Robert Johansson shows you how to leverage the numerical and mathematical modules in Python and its Standard Library as well as popular open source numerical Python packages like NumPy, FiPy, matplotlib and more to numerically compute solutions and mathematically model applications in a number of areas like big data, cloud computing, financial engineering, business managemen…
- Edition
- 1
- ISBN/ISSN
- 978-1-4842-0553-2
- Collation
- XXII, 487
- Series Title
- -
- Call Number
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Numerical Methods in Matrix Computations
Matrix algorithms are at the core of scientific computing and are indispensable tools in most applications in engineering. This book offers a comprehensive and up-to-date treatment of modern methods in matrix computation. It uses a unified approach to direct and iterative methods for linear systems, least squares and eigenvalue problems. A thorough analysis of the stability, accuracy, and compl…
- Edition
- 1
- ISBN/ISSN
- 978-3-319-05088-1
- Collation
- XVI, 800
- Series Title
- Texts in Applied Mathematics
- Call Number
- -
Numerical Mathematics and Advanced Applications - ENUMATH 2013:Proceedings of…
This book gathers a selection of invited and contributed lectures from the European Conference on Numerical Mathematics and Advanced Applications (ENUMATH) held in Lausanne, Switzerland, August 26-30, 2013. It provides an overview of recent developments in numerical analysis, computational mathematics and applications from leading experts in the field. New results on finite element methods, mul…
- Edition
- 1
- ISBN/ISSN
- 978-3-319-10704-2
- Collation
- XVI, 810
- Series Title
- Lecture Notes in Computational Science and Engineering
- Call Number
- -
Numerical Analysis Using Sage
This is the first numerical analysis text to use Sage for the implementation of algorithms and can be used in a one-semester course for undergraduates in mathematics, math education, computer science/information technology, engineering, and physical sciences. The primary aim of this text is to simplify understanding of the theories and ideas from a numerical analysis/numerical methods course vi…
- Edition
- 1
- ISBN/ISSN
- 978-3-319-16738-1
- Collation
- XII, 314
- Series Title
- Springer Undergraduate Texts in Mathematics and Technology
- Call Number
- -
Nonlinear Mode Decomposition:Theory and Applications
This work introduces a new method for analysing measured signals: nonlinear mode decomposition, or NMD. It justifies NMD mathematically, demonstrates it in several applications and explains in detail how to use it in practice. Scientists often need to be able to analyse time series data that include a complex combination of oscillatory modes of differing origin, usually contaminated by random f…
- Edition
- 1
- ISBN/ISSN
- 978-3-319-20015-6
- Collation
- XXIII, 135
- Series Title
- Springer Theses
- Call Number
- -
MATLAB Recipes: A Problem-Solution Approach
Learn from state-of-the-art examples in robotics, motors, detection filters, chemical processes, aircraft, and spacecraft. This is a practical reference for industry engineers using MATLAB to solve everyday problems. With MATLAB Recipes: A Problem-Solution Approach you will review contemporary MATLAB coding including the latest language features and use MATLAB as a software development environm…
- Edition
- -
- ISBN/ISSN
- 978-1-4842-0559-4
- Collation
- -
- Series Title
- -
- Call Number
- -
Vector Generalized Linear and Additive Models With an Implementation in R
This book presents a greatly enlarged statistical framework compared to generalized linear models (GLMs) with which to approach regression modelling. Comprising of about half-a-dozen major classes of statistical models, and fortified with necessary infrastructure to make the models more fully operable, the framework allows analyses based on many semi-traditional applied statistics models to be …
- Edition
- -
- ISBN/ISSN
- 978-1-4939-2818-7
- Collation
- XXIV, 589
- Series Title
- -
- Call Number
- -
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