English | 2022 | ISBN: 1804616885 | 460 pages | True PDF EPUB | 30.76 MB

Get to grips with constructing state of the art simulation models with python.

Key Features

Understand various statistical and physical simulations to improve systems using Python
Learn to create a 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 environment

Book Description

This book is a comprehensive guide to understand various computational statistical simulations using Python.

This book will start with the required foundation to understand various methods and techniques to delve into complex topics. Developers working with simulation models will be able to put their knowledge to work with this practical guide. The book provides a hands-on approach to implementation and associated methodologies that will have you up-and-running, and productive in no .

Complete with step-by-step explanations of essential concepts, practical examples and self-assessment questions, you will b by exploring the numerical simulation algorithms, including an overview of relevant applications. You'll learn how to use Python to develop simulation model and understand how to use the several Python packages. You will then explore various numerical simulation algorithms and concepts, such as Markov Decision Process, Monte Carlo methods, and bootstrapping techniques. By the end of this book, you will be able to construct simulation models.
What you will learn

Get to grips with the concepts of randomness and data generation process
Delve into Resampling methods
Learn how to work with Monte Carlo Simulations
Use simulation to improve or optimize systems
Learn how to run efficient simulations to analyze real-world systems
Learn to run efficient simulations to analyze real-world systems

Who This Book Is For

This book is for Data Scientists, simulation eeers, or anyone who is already familiar with the basic computational methods but now wants to implement various simulation techniques such as Monte-Carlo methods, statistical simulation using Python.



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