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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.
Several theories and applications are provided, each with working Python scripts.
All Python functions written for this book are archived on GitHub.
Readers do not have to be Python experts, but a working knowledge of the language is required. Students who want to know more about the foundations of modeling and simulation will find this an educational and foundational resource.
Table of Contents
2. Random Values
3. Application of Random Values
4. The Monte Carlo Method
5. Modeling Self-Organization
6. Hidden Markov Models
7. Identification of Start Codons
8. HMM Application in Baseball
9. Hidden Shakespeare Model
10. Connected Data
11. Gene Expression Arrays
12. Simultaneous Equations
13. Simulations of Motion
15. Coupled Differential Equations
16. Extraordinary Number of Solutions
17. Agent Based Modeling - Virus Spread