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Key benefits

Understand various statistical and physical simulations to improve systems using Python

Learn to create the numerical prototype of a real model using hands-on examples

Evaluate performance and output results based on how the prototype would work in the real world

Description

Simulation modelling is an exploration method that aims to imitate physical systems in a virtual environment and retrieve useful statistical inferences from it. The ability to analyze the model as it runs sets simulation modelling apart from other methods used in conventional analyses. This book is your comprehensive and hands-on guide to understanding various computational statistical simulations using Python. The book begins by helping you get familiarized with the fundamental concepts of simulation modelling, that’ll enable you to understand the various methods and techniques needed to explore complex topics. Data scientists working with simulation models will be able to put their knowledge to work with this practical guide. As you advance, you’ll dive deep into numerical simulation algorithms, including an overview of relevant applications, with the help of real-world use cases and practical examples. You'll also find out how to use Python to develop simulation models and how to use several Python packages. Finally, you’ll get to grips with various numerical simulation algorithms and concepts, such as Markov Decision Processes, Monte Carlo methods, and bootstrapping techniques. 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

Get to grips with the concept of randomness and the data generation process

Delve into resampling methods

Discover how to work with Monte Carlo simulations

Utilize simulations to improve or optimize systems

Find out how to run efficient simulations to analyze real-world systems

Understand how to simulate random walks using Markov chains

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Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

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Frequently bought together

Modern Time Series Forecasting with Python

$52.99

$49.99

Hands-On Simulation Modeling with Python

$49.99

Total $ 152.97

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Amazon verified reviews

Steven Fernandes Jan 31, 2023

The book starts with an introduction to numerical simulation and simulation models. It covers understanding randomness, random numbers, the basics of probability, and data generation processes. Any hands-on book won't be complete without exploring Monte Carlo simulations, Markov decision processes and resampling methods, and evolutionary systems. Finally, the book covers simulation applications to solve real-world problems for financial engineering and physical phenomena using neural networks. The book ends by providing modeling and simulation for project management and fault diagnosis in dynamic systems.

Amazon Verified review

Devanshu Mar 21, 2023

In this book, the abecedarian concept of running simulations is introduced. Using Matlab, R, and Python is a skill that the author has acquired over the course of 15 years. I enjoyed reading this book and completing the reciprocal coding exercises provided by the content behind each chapter. This title is highly recommended for everyone! Hope it will assist you in achieving what you are looking for in your data wisdom career. I have gained a great deal of experience in understanding different types of simulation styles. Furthermore, the book helps me grasp the generalities and be capable of applying them when needed. Aside from that, it provides a new perspective on what I used to miss out on when I learned Monte Carlo simulation.

Yiqiao Yin Jan 22, 2023

This book introduces the fundamental concept of running simulations. The author has 15+ years of experience using Matlab, R, and Python and he has 2 masters and 1 phd in renowed fields, which is a great title with high honor given. I really enjoyed this book and the complementary coding exercises given by the content behind each chapters. I highly recommend this title to everyone! Hopefully it'll be helpful for you to achieve what you are looking for in your data science career.I have gained a lot of experience in knowing different types of simulation methods. In addition, the book helps me grip the concepts and be able to wield them at my command whenever desired. On top of that, it shed new light of what I used to miss out when I was learning monte carlo simulation.

gbushnell Jan 27, 2023

This book is a great resource for anyone looking to learn about simulation modeling. It gives a strong foundation around computational statistical simulations, and takes a hands-on approach to implementation. The step-by-step explanations, practical examples, and self-assessment questions make it easy to understand and apply the concepts.The book covers does a great job of covering numerical simulation algorithms, Markov Decision Processes, Monte Carlo methods, bootstrapping. Overall, the book is a great tool for anyone who wants to learn how to build simulation models with Python, and understand the overall fundamentals of simulation modeling. Would highly recommend.

Dror Mar 09, 2023

In this era of "unreasonable effectiveness of data", computational simulation is becoming ever more important as virtually all engineering fields become more data-driven. Understanding simulation modeling as a general engineering tool is a non-trivial task, however, as most resources available tend to focus on applications in specific and limited domains.This unique book takes a refreshing approach and provides a broad and deep practical overview on simulation modeling as a general computational tool. It begins with covering the fundamentals of numerical simulation, such as random number generation and probability. It then provides a comprehensive description of various simulation modeling and techniques, such as Monte Carlo simulation, Markov decision processes (MDPs), resampling methods, and evolutionary systems. The final part of the book presents a wide range of practical, real-world simulation applications in various domains, such as financial engineering, physics and dynamical systems.In addition, the book takes a very practical, hands-on approach, and the provided code examples (which use the Python programming language) are extremely helpful in demonstrating the implementation of the presented methods and techniques. Note that some basic familiarity with the Python programming language is recommended.This useful guide will benefit any data scientist, physicist, engineer or practitioner looking to leverage computational statistical simulations in their work. Highly recommended!

Hands-On Simulation Modeling with Python: Develop simulation models for improved efficiency and precision in the decision-making process , Second Edition

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