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Modeling and Simulation in Python introduces fundamental computational modeling techniques that are used in a variety of science and engineering disciplines. It emphasizes algorithmic thinking skills using different computational environments, and includes a number of interesting examples, including Shakespeare, movie databases, and virus spread.
The use of Python as a powerful computational tool is expanding with great strides. Python is a language which is easy to use, and the libraries of tools provides it with efficient versatility. As the tools continue to expand, users can create insightful models and simulations. While the tools offer an easy method to create a pipeline, such constructions are not guaranteed to provide correct results. A lot of things can go wrong when building a simulation - deviously so. Users need to understand more than just how to build a process pipeline.
Modeling and Simulation in Python introduces fundamental computational modeling techniques that are used in a variety of science and engineering disciplines. It emphasizes algorithmic thinking skills using different computational environments, and includes a number of interesting examples, including Shakespeare, movie databases, virus spread, and Chess.
Key Features:
Auteur
Jason M. Kinser is Chair of the Department of Computational and Data Sciences at George Mason University. Dr. Kinser has taught more than 30 different courses at Mason in several departments including Computational & Data Sciences, Physics, Bioinformatics, Forensics, and even Biology. His research interests include analysis of images, fusion of image content non-image data, and education.
Texte du rabat
Modeling and Simulation in Python introduces fundamental computational modeling techniques that are used in a variety of science and engineering disciplines. It emphasizes algorithmic thinking skills using different computational environments, and includes a number of interesting examples, including Shakespeare, movie databases, and virus spread.
Contenu
Preface
Software
Introduction
Random Values
Application of Random Values
The Monte Carlo Method
Modeling Self-Organization
Hidden Markov Models
Identification of Start Codons
HMM Application in Baseball
Hidden Shakespeare Model
Connected Data
Gene Expression Arrays
Simultaneous Equations
Simulations of Motion
Oscillations
Coupled Differential Equations
Extraordinary Number of Solutions
Agent Based Modeling - Virus Spread
Chess
Bibliography
Index