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Key benefits

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

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.

Who is this book 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.

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

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Frequently bought together

40 Algorithms Every Programmer Should Know

€37.99

Hands-On Simulation Modeling with Python

€48.99

Machine Learning for Algorithmic Trading

€43.99

Total € 130.97

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Amazon verified reviews

TJD Dec 29, 2020

Great book to review simulation models using python!

Amazon Verified review

Hash Jan 09, 2021

Hands-on Simulation Modeling with Python is all about implementing some of the most famous simulation techniques by Python using standard packages like NumPy, SciPy, Scikit-learn, Pandas, and Matplotlib.Since most of the simulations in the book are based on random processes like Mont Carlo and Brownian motion, the book includes an introduction to mathematical concepts like statistics, probability (e.g., the law of large numbers, central limit theorem, and Markov chain).In addition, some non-statistical approaches like neural networks and numerical optimizations such as Newton-Raphson and gradient descent are discussed in the book.In the latest chapters, modeling of real-world applications such as stock price prediction, risk management, project management, and what-if analysis are taught.This book can be used as a standard coursebook for simulation and modeling or just to learn simulation with python.

Mahshad Samnejad Dec 23, 2020

Python is increasingly more dominant in the world of programming language, and a book bringing modeling and simulation to the world of python is an absolute necessity.

Jagannath Banerjee Dec 16, 2020

Hands-On Simulation Modeling with Python is a comprehensive book covering modelling and simulation focused for model and simulation designers and engineers . Books covers the fundamentals and implementation of modelling and simulation using Python and standard data science packages like numpy, matplotlib and statsmodel.This is not a beginners book and reader needs background in Python, statistics and some modelling concepts.Book begins with fundamentals of statistics and probability covering random number generation methods, data distributions , probability density functions, stationarity, uniformity and various distributions. It naturally flows into Monte Carlo Simulations and Markov Decision process. Additionally it covers how to simulate the phenomena using Neural Networks. I liked chapter 8 and chapter 10 that implicitly walks us through real life use case of modelling and simulating stock market and risk assessment and also how we can model the project management side.As I was reading chapter 4 & 5, I was able to use random number generation, distribution and simulation with Monte Carlo concepts into demand forecasting project efficiently.What I liked about the book ? • Book possess great balance of theory , math and actual implementation using Python standard libraries. • Authors did great job in explaining the statistical concepts with examples, references and workable codes and images. • Flow of concepts from Chapter -1 to end looked okay to me and I was able to follow along and implement The codes and as mentioned above. • I specially loved chapter 4 - Monte Carlo Simulations and chapter -5 Markov decision process. They had great details from concept to implementation and I implemented the concepts into my project.Overall, the book is well written and covers significant details on simulation and modelling . I feel this book can alternatively be used for machine learning testing, proof of concept and pilot for many areas if data is missing or data is not ready yet for actual work.

jml Dec 10, 2020

Hands-on Simulation Modeling (HOSM) packs three disciplines — statistics, modeling, and application development — into a single work. Not for the casual reader, this book not only presents the topics and the math behind them in a clearly-organized, lucid manner but also demonstrates the use of numpy, matplotlib, and other freely-available Python modules to calculate and display simulation results. The book’s well-organized and moves from topic to topic seamlessly, although it’s so full of information that the reader might be advised to plan on taking it in small chunks. It’d make a superb textbook for a simulation/modeling class. The final chapters cover three real-world applications (financial modeling, physical system simulation via neural networking, and simulation for project management) in sufficient depth that the reader could apply the techniques rapidly upon studying the material. A wrap-up chapter also drives home the potential application of the modeling techniques in a number of environments, providing a good overview and jumping-off points for continued learning.If you’re looking for in-depth coverage of simulation modeling with a Python application focus, this book’s definitely worth checking out. It’s much more readable than a typical textbook on the subject, although concentration’s required to get the most out of it.

Hands-On Simulation Modeling with Python: Develop simulation models to get accurate results and enhance decision-making processes

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