Hands-On Simulation Modeling with Python: Develop simulation models to get accurate results and enhance decision-making processes
by: Giuseppe CiaburroPrint Length 页数: 346 pages
Publisher finelybook 出版社: Packt Publishing (17 July 2020)
Language 语言: English
ISBN-10: 1838985093
ISBN-13: 9781838985097
Book Description
Enhance your simulation modeling skills by: creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guide
Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide,you’ll understand various computational statistical simulations using Python.
Starting with the fundamentals of simulation modeling,you’ll understand concepts such as randomness and explore data generating processes,resampling methods,and bootstrapping techniques. You’ll then cover key algorithms such as Monte Carlo simulations and Markov decision processes,which are used to develop numerical simulation models,and discover how they can be used to solve real-world problems. As you advance,you’ll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques,you’ll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering,prototyping project management to improve planning,and simulating physical phenomena using neural networks.
By the end of this book,you’ll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.
What you will learn
Gain an overview of the different types of simulation models
Get to grips with the concepts of randomness and data generation process
Understand how to work with discrete and continuous distributions
Work with Monte Carlo simulations to calculate a definite integral
Find out how to simulate random walks using Markov chains
Obtain robust estimates of confidence intervals and standard errors of population parameters
Discover how to use optimization methods in real-life applications
Run efficient simulations to analyze real-world systems
