Hands On Simulation Modeling With Python Develop Simulation Models to Get Accurate Results and Enhance Decision Making Processes 1st Edition by Giuseppe Ciaburro 1838985093 9781838985097

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ISBN 10: 1838985093

ISBN 13: 9781838985097

Author: Giuseppe Ciaburro

Enhance your simulation modeling skills by creating and analyzing digital prototypes of a physical model using Python programming with this comprehensive guide Key Features 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 Book 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. 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 Who this book is 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.

Hands On Simulation Modeling With Python Develop Simulation Models to Get Accurate Results and Enhance Decision Making Processes 1st Table of contents:

Section 1: Getting Started with Numerical Simulation

Chapter 1: Introducing Simulation Models

Introducing simulation models

Decision-making workflow

Comparing modeling and simulation

Pros and cons of simulation modeling

Simulation modeling terminology

Classifying simulation models

Comparing static and dynamic models

Comparing deterministic and stochastic models

Comparing continuous and discrete models

Approaching a simulation-based problem

Problem analysis

Data collection

Setting up the simulation model

Simulation software selection

Verification of the software solution

Validation of the simulation model

Simulation and analysis of results

Dynamical systems modeling

Managing workshop machinery

Simple harmonic oscillator

Predator-prey model

Summary

Chapter 2: Understanding Randomness and Random Numbers

Technical requirements

Stochastic processes

Types of stochastic process

Examples of stochastic processes

The Bernoulli process

Random walk

The Poisson process

Random number simulation

Probability distribution

Properties of random numbers

The pseudorandom number generator

The pros and cons of a random number generator

Random number generation algorithms

Linear congruential generator

Random numbers with uniform distribution

Lagged Fibonacci generator

Testing uniform distribution

The chi-squared test

Uniformity test

Exploring generic methods for random distributions

The inverse transform sampling method

The acceptance-rejection method

Random number generation using Python

Introducing the random module

The random.random() function

The random.seed() function

The random.uniform() function

The random.randint() function

The random.choice() function

The random.sample() function

Generating real-valued distributions

Summary

Chapter 3: Probability and Data Generation Processes

Technical requirements

Explaining probability concepts

Types of events

Calculating probability

Probability definition with an example

Understanding Bayes’ theorem

Compound probability

Bayes’ theorem

Exploring probability distributions

Probability density function

Mean and variance

Uniform distribution

Binomial distribution

Normal distribution

Summary

Section 2: Simulation Modeling Algorithms and Techniques

Chapter 4: Exploring Monte Carlo Simulations

Technical requirements

Introducing Monte Carlo simulation

Monte Carlo components

First Monte Carlo application

Monte Carlo applications

Applying the Monte Carlo method for Pi estimation

Understanding the central limit theorem

Law of large numbers

Central limit theorem

Applying Monte Carlo simulation

Generating probability distributions

Numerical optimization

Project management

Performing numerical integration using Monte Carlo

Defining the problem

Numerical solution

Min-max detection

Monte Carlo method

Visual representation

Summary

Chapter 5: Simulation-Based Markov Decision Processes

Technical requirements

Overview of Markov processes

The agent-environment interface

Exploring MDPs

Understanding the discounted cumulative reward

Comparing exploration and exploitation concepts

Introducing Markov chains

Transition matrix

Transition diagram

Markov chain applications

Introducing random walks

Simulating a one-dimensional random walk

Simulating a weather forecast

The Bellman equation explained

Dynamic programming concepts

Principle of optimality

The Bellman equation

Multi-agent simulation

Summary

Chapter 6: Resampling Methods

Technical requirements

Introducing resampling methods

Sampling concepts overview

Reasoning about sampling

Pros and cons of sampling

Probability sampling

How sampling works

Exploring the Jackknife technique

Defining the Jackknife method

Estimating the coefficient of variation

Applying Jackknife resampling using Python

Demystifying bootstrapping

Introducing bootstrapping

Bootstrap definition problem

Bootstrap resampling using Python

Comparing Jackknife and bootstrap

Explaining permutation tests

Approaching cross-validation techniques

The validation set approach

Leave-one-out cross validation

K-fold cross validation

Cross-validation using Python

Summary

Chapter 7: Using Simulation to Improve and Optimize Systems

Technical requirements

Introducing numerical optimization techniques

Defining an optimization problem

Explaining local optimality

Defining the descent methods

Approaching the gradient descent algorithm

Understanding the learning rate

Explaining the trial and error method

Implementing gradient descent in Python

Facing the Newton-Raphson method

Using the Newton-Raphson algorithm for root-finding

Approaching Newton-Raphson for numerical optimization

Applying the Newton-Raphson technique

Deepening our knowledge of stochastic gradient descent

Discovering the multivariate optimization methods in Python

The Nelder–Mead method

Powell’s conjugate direction algorithm

Summarizing other optimization methodologies

Summary

Section 3: Real-World Applications

Chapter 8: Using Simulation Models for Financial Engineering

Technical requirements

Understanding the geometric Brownian motion model

Defining a standard Brownian motion

Addressing the Wiener process as random walk

Implementing a standard Brownian motion

Using Monte Carlo methods for stock price prediction

Exploring the Amazon stock price trend

Handling the stock price trend as time series

Introducing the Black-Scholes model

Applying Monte Carlo simulation

Studying risk models for portfolio management

Using variance as a risk measure

Introducing the value-at-risk metric

Estimating the VaR for some NASDAQ assets

Summary

Chapter 9: Simulating Physical Phenomena Using Neural Networks

Technical requirements

Introducing the basics of neural networks

Understanding biological neural networks

Exploring ANNs

Understanding feedforward neural networks

Exploring neural network training

Simulating airfoil self-noise using ANNs

Importing data using pandas

Scaling the data using sklearn

Viewing the data using matplotlib

Splitting the data

Explaining multiple linear regression

Understanding a multilayer perceptron regressor model

Exploring deep neural networks

Getting familiar with convolutional neural networks

Examining recurrent neural networks

Analyzing LSTM networks

Summary

Chapter 10: Modeling and Simulation for Project Management

Technical requirements

Introducing project management

Understanding what-if analysis

Managing a tiny forest problem

Summarizing the Markov decision process

Exploring the optimization process

Introducing MDPtoolbox

Defining the tiny forest management example

Addressing management problems using MDPtoolbox

Changing the probability of fire

Scheduling project time using Monte Carlo simulation

Defining the scheduling grid

Estimating the task’s time

Developing an algorithm for project scheduling

Exploring triangular distribution

Summary

Chapter 11: What’s Next?

Summarizing simulation modeling concepts

Generating random numbers

Applying Monte Carlo methods

Addressing the Markov decision process

Analyzing resampling methods

Exploring numerical optimization techniques

Using artificial neural networks for simulation

Applying simulation model to real life

Modeling in healthcare

Modeling in financial applications

Modeling physical phenomenon

Modeling public transportation

Modeling human behavior

Next steps for simulation modeling

Increasing the computational power

Machine learning-based models

Automated generation of simulation models

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