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

Learn to construct state-of-the-art simulation models with Python and enhance your simulation modelling skills, as well as create and analyze digital prototypes of physical models with ease

Key Features:

• 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

Book 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

Who this book is for:

This book is for data scientists, simulation engineers, and anyone who is already familiar with the basic computational methods and wants to implement various simulation techniques such as Monte-Carlo methods and statistical simulation using Python.

學習使用Python建立最先進的模擬模型，提升模擬建模技能，輕鬆創建和分析物理模型的數位原型。

主要特點：
- 了解使用Python改善系統的各種統計和物理模擬
- 通過實際示例學習創建真實模型的數值原型
- 根據原型在真實世界中的運作評估性能和輸出結果

書籍描述：
模擬建模是一種探索方法，旨在在虛擬環境中模擬物理系統並從中獲取有用的統計推斷。分析模型運行時的能力使模擬建模與傳統分析方法有所不同。本書是您全面且實用的指南，以了解使用Python進行各種計算統計模擬。

本書首先幫助您熟悉模擬建模的基本概念，這將使您能夠理解探索複雜主題所需的各種方法和技術。與模擬模型一起工作的數據科學家將能夠通過這本實用指南將他們的知識應用到實際工作中。隨著進一步的學習，您將深入研究數值模擬算法，包括相關應用的概述，並通過實際案例和實例了解如何使用Python開發模擬模型以及如何使用多個Python套件。最後，您將掌握各種數值模擬算法和概念，例如馬爾可夫決策過程、蒙特卡羅方法和自助法。

通過閱讀本書，您將學習如何構建和部署自己的模擬模型，以應對現實世界的挑戰。

學到的內容：
- 理解隨機性和數據生成過程
- 深入研究重抽樣方法
- 發現如何使用蒙特卡羅模擬
- 利用模擬改進或優化系統
- 了解如何運行高效的模擬以分析現實世界系統
- 理解如何使用馬爾可夫鏈模擬隨機遊走

本書適合數據科學家、模擬工程師以及已經熟悉基本計算方法並希望使用Python實現各種模擬技術（如蒙特卡羅方法和統計模擬）的讀者。