book

Hands-On Simulation Modeling with Python

Name: Hands-On Simulation Modeling with Python
Author: Giuseppe Ciaburro
ISBN: 9781838985097

by Giuseppe Ciaburro

July 2020

Intermediate to advanced

346 pages

7h 58m

English

Packt Publishing

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Hands-On Simulation Modeling with Python
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Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesDownload the color imagesConventions usedGet in touchReviews
Section 1: Getting Started with Numerical Simulation
Chapter 1: Introducing Simulation Models
Introducing simulation models Decision-making workflowComparing modeling and simulationPros and cons of simulation modelingSimulation modeling terminologyClassifying simulation modelsComparing static and dynamic modelsComparing deterministic and stochastic modelsComparing continuous and discrete modelsApproaching a simulation-based problemProblem analysisData collectionSetting up the simulation modelSimulation software selectionVerification of the software solutionValidation of the simulation modelSimulation and analysis of resultsDynamical systems modelingManaging workshop machinerySimple harmonic oscillatorPredator-prey modelSummary
Chapter 2: Understanding Randomness and Random Numbers
Technical requirements Stochastic processesTypes of stochastic processExamples of stochastic processesThe Bernoulli processRandom walkThe Poisson processRandom number simulationProbability distributionProperties of random numbersThe pseudorandom number generatorThe pros and cons of a random number generatorRandom number generation algorithmsLinear congruential generatorRandom numbers with uniform distributionLagged Fibonacci generatorTesting uniform distributionThe chi-squared testUniformity testExploring generic methods for random distributionsThe inverse transform sampling method The acceptance-rejection methodRandom number generation using PythonIntroducing the random moduleThe random.random() functionThe random.seed() functionThe random.uniform() functionThe random.randint() functionThe random.choice() functionThe random.sample() functionGenerating real-valued distributionsSummary
Chapter 3: Probability and Data Generation Processes
Technical requirements Explaining probability concepts Types of eventsCalculating probabilityProbability definition with an exampleUnderstanding Bayes' theoremCompound probabilityBayes' theoremExploring probability distributionsProbability density functionMean and varianceUniform distributionBinomial distributionNormal distributionSummary
Section 2: Simulation Modeling Algorithms and Techniques
Chapter 4: Exploring Monte Carlo Simulations
Technical requirements Introducing Monte Carlo simulationMonte Carlo componentsFirst Monte Carlo applicationMonte Carlo applications Applying the Monte Carlo method for Pi estimationUnderstanding the central limit theoremLaw of large numbersCentral limit theorem Applying Monte Carlo simulationGenerating probability distributionsNumerical optimizationProject managementPerforming numerical integration using Monte CarloDefining the problemNumerical solutionMin-max detectionMonte Carlo methodVisual representationSummary
Chapter 5: Simulation-Based Markov Decision Processes
Technical requirements Overview of Markov processesThe agent-environment interfaceExploring MDPsUnderstanding the discounted cumulative reward Comparing exploration and exploitation conceptsIntroducing Markov chainsTransition matrixTransition diagramMarkov chain applicationsIntroducing random walksSimulating a one-dimensional random walkSimulating a weather forecastThe Bellman equation explainedDynamic programming concepts Principle of optimalityThe Bellman equationMulti-agent simulationSummary
Chapter 6: Resampling Methods
Technical requirements Introducing resampling methods Sampling concepts overviewReasoning about sampling Pros and cons of samplingProbability samplingHow sampling worksExploring the Jackknife techniqueDefining the Jackknife methodEstimating the coefficient of variation Applying Jackknife resampling using PythonDemystifying bootstrapping Introducing bootstrappingBootstrap definition problemBootstrap resampling using PythonComparing Jackknife and bootstrapExplaining permutation testsApproaching cross-validation techniquesThe validation set approachLeave-one-out cross validationK-fold cross validationCross-validation using PythonSummary

Chapter 7: Using Simulation to Improve and Optimize Systems
Technical requirements Introducing numerical optimization techniquesDefining an optimization problemExplaining local optimality Defining the descent methodsApproaching the gradient descent algorithmUnderstanding the learning rateExplaining the trial and error methodImplementing gradient descent in PythonFacing the Newton-Raphson methodUsing the Newton-Raphson algorithm for root-finding Approaching Newton-Raphson for numerical optimizationApplying the Newton-Raphson technique Deepening our knowledge of stochastic gradient descentDiscovering the multivariate optimization methods in PythonThe Nelder–Mead methodPowell's conjugate direction algorithmSummarizing other optimization methodologiesSummary
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 motionAddressing the Wiener process as random walkImplementing a standard Brownian motionUsing Monte Carlo methods for stock price predictionExploring the Amazon stock price trendHandling the stock price trend as time seriesIntroducing the Black-Scholes modelApplying Monte Carlo simulationStudying risk models for portfolio managementUsing variance as a risk measureIntroducing the value-at-risk metricEstimating the VaR for some NASDAQ assetsSummary
Chapter 9: Simulating Physical Phenomena Using Neural Networks
Technical requirements Introducing the basics of neural networks Understanding biological neural networksExploring ANNsUnderstanding feedforward neural networksExploring neural network trainingSimulating airfoil self-noise using ANNsImporting data using pandasScaling the data using sklearnViewing the data using matplotlibSplitting the data Explaining multiple linear regressionUnderstanding a multilayer perceptron regressor modelExploring deep neural networksGetting familiar with convolutional neural networksExamining recurrent neural networksAnalyzing LSTM networksSummary
Chapter 10: Modeling and Simulation for Project Management
Technical requirements Introducing project managementUnderstanding what-if analysisManaging a tiny forest problemSummarizing the Markov decision processExploring the optimization processIntroducing MDPtoolboxDefining the tiny forest management exampleAddressing management problems using MDPtoolboxChanging the probability of fireScheduling project time using Monte Carlo simulationDefining the scheduling gridEstimating the task's timeDeveloping an algorithm for project schedulingExploring triangular distributionSummary
Chapter 11: What's Next?
Summarizing simulation modeling concepts Generating random numbersApplying Monte Carlo methodsAddressing the Markov decision processAnalyzing resampling methodsExploring numerical optimization techniquesUsing artificial neural networks for simulationApplying simulation model to real lifeModeling in healthcareModeling in financial applicationsModeling physical phenomenonModeling public transportationModeling human behaviorNext steps for simulation modelingIncreasing the computational powerMachine learning-based models Automated generation of simulation modelsSummary
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Overview

Hands-On Simulation Modeling with Python is your go-to resource for mastering the art of building simulation models with Python. This book shows you how to analyze, optimize, and predict real-world systems' behavior using cutting-edge computational techniques. You'll gain practical insights and hands-on experience to create Python-based models that enhance decision-making processes.

What this Book will help me do

Understand the basics of simulation modeling and randomness concepts for statistical simulations.
Learn to implement Monte Carlo and Markov decision processes for complex problem solving.
Gain proficiency in optimization techniques to improve system performance and resource utilization.
Explore industry-focused applications of simulation, including financial engineering and neural network simulations.
Develop and deploy your own Python-based simulation models for real-world scenarios.

Author(s)

Giuseppe Ciaburro is an experienced author and data scientist with expertise in simulation modeling, applied machine learning, and optimization techniques. With years of hands-on experience in leveraging Python for solving complex problems, Giuseppe is passionate about making technical concepts more approachable for learners. Through engaging examples and methodical explanations, he delivers practical insights into simulation modeling.

Who is it for?

This book is ideal for professionals such as simulation developers, engineers, and model makers who are already familiar with the basics of computational methods and aim to gain deeper intuition into simulation modeling. If you are looking to enhance your skills in implementing statistical simulations and Python-based numerical methods, this guide is for you. A working knowledge of Python is expected to get the most out of it.

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