Paperback. Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guideKey FeaturesLearn to create a digital prototype of a real model using hands-on examplesEvaluate the performance and output of your prototype using simulation modeling techniquesUnderstand various statistical and physical simulations to improve systems using PythonBook DescriptionSimulation 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 learnGain an overview of the different types of simulation modelsGet to grips with the concepts of randomness and data generation processUnderstand how to work with discrete and continuous distributionsWork with Monte Carlo simulations to calculate a definite integralFind out how to simulate random walks using Markov chainsObtain robust estimates of confidence intervals and standard errors of population parametersDiscover how to use optimization methods in real-life applicationsRun efficient simulations to analyze real-world systemsWho this book is forHands-On Simulation Modeling with Python is for simulation developers and engineers, model designers, and anyone already familiar with the basic computational methods that are used to study the behavior of systems. This book will help you explore advanced simulation techniques such as Monte Carlo methods, statistical simulations, and much more using Python. Working knowledge of Python programming language is required. Developers working with the simulation models will be able to put their knowledge to work with this practical guide. You will work with real-world data to uncover various patterns used in complex systems using Python. The book provides a hands-on approach to implementation and associated methodologies to improve or optimize systems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo: Hands-On Simulation Modeling with Python (Paperback)
Editor: Packt Publishing Limited, Birmingham
Fecha de publicaci�n: 2020
Idioma: Ingl�s
ISBN 10: 1838985093
ISBN 13: 9781838985097
Encuadernaci�n: Paperback
Estado: new

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Sinopsis

Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guide

Key Features

Learn to create a digital prototype of a real model using hands-on examples
Evaluate the performance and output of your prototype using simulation modeling techniques
Understand various statistical and physical simulations to improve systems using Python

Book Description

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

Who this book is for

Hands-On Simulation Modeling with Python is for simulation developers and engineers, model designers, and anyone already familiar with the basic computational methods that are used to study the behavior of systems. This book will help you explore advanced simulation techniques such as Monte Carlo methods, statistical simulations, and much more using Python. Working knowledge of Python programming language is required.

Introducing Simulation Models
Understanding Randomness and Random Numbers
Probability and Data Generating Processes
Exploring Monte Carlo Simulations
Simulation-Based Markov Decision Process
Resampling Methods
Using Simulations to Improve and Optimize Systems
Using Simulation Models for Financial Engineering
Simulating Physical Phenomena Using Neural Networks
Modeling and Simulation for Project Management
What's Next?

Acerca del autor

Giuseppe Ciaburro holds a PhD in environmental technical physics, along with two master's degrees. His research was focused on machine learning applications in the study of urban sound environments. He works at the Built Environment Control Laboratory at the Universit� degli Studi della Campania Luigi Vanvitelli, Italy. He has over 18 years' professional experience in programming (Python, R, and MATLAB), first in the field of combustion, and then in acoustics and noise control. He has several publications to his credit.

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