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

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!

This book starts with defining different types of simulations and then explains the math of generating random number and generating random variate using different statistical distributions. After it dives into simulation algorithms. One thing I liked the most was focusing on monte carlo and markov chain simulations which will be used in reinforcement learning.I wish this book was also using Simpy package for discrete event simulation. Many data scientists use Simpy package for developing dynamic simulation model and it would been great if this book was covering it as well.

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.

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.

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.