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Hands-On Simulation Modeling with Python - Second Edition

Name: Hands-On Simulation Modeling with Python - Second Edition
Author: Giuseppe Ciaburro
ISBN: 9781804616888

by Giuseppe Ciaburro

November 2022

Intermediate to advanced

460 pages

12h 44m

English

Packt Publishing

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Hands-On Simulation Modeling with Python
ContributorsAbout the authorAbout the reviewer
Preface
Who this book is forWhat this book coversTo get the most out of this bookDownload the example code filesConventions usedGet in touchShare Your ThoughtsDownload a free PDF copy of this book
Part 1:Getting Started with Numerical Simulation
Chapter 1: Introducing Simulation Models
Technical requirementsIntroducing 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 resultsExploring Discrete Event Simulation (DES)Finite-state machine (FSM)State transition table (STT)State transition graph (STG)Dynamic systems modelingManaging workshop machinerySimple harmonic oscillatorThe predator-prey modelHow to run efficient simulations to analyze real-world systemsSummary
Chapter 2: Understanding Randomness and Random Numbers
Technical requirements Stochastic processesTypes of stochastic processesExamples 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 distributionChi-squared testUniformity testExploring generic methods for random distributionsThe inverse transform sampling method The acceptance-rejection methodRandom number generation using PythonIntroducing the random moduleGenerating real-value distributionsRandomness requirements for securityPassword-based authentication systemsRandom password generatorCryptographic random number generatorIntroducing cryptographyRandomness and cryptographyEncrypted/decrypted message generator Summary
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 distributionsThe probability density functionMean and varianceUniform distributionBinomial distributionNormal distributionGenerating synthetic dataReal data versus artificial dataSynthetic data generation methodsData generation with KerasData augmentationSimulation of power analysisThe power of a statistical testPower analysisSummary
Part 2:Simulation Modeling Algorithms and Techniques
Chapter 4: Exploring Monte Carlo Simulations
Technical requirements Introducing the Monte Carlo simulationMonte Carlo componentsFirst Monte Carlo applicationMonte Carlo applications Applying the Monte Carlo method for Pi estimationUnderstanding the central limit theoremLaw of large numbersThe central limit theorem Applying the Monte Carlo simulationGenerating probability distributionsNumerical optimizationProject managementPerforming numerical integration using Monte CarloDefining the problemNumerical solutionMin-max detectionThe Monte Carlo methodVisual representationExploring sensitivity analysis conceptsLocal and global approachesSensitivity analysis methodsSensitivity analysis in actionExplaining the cross-entropy methodIntroducing cross-entropyCross-entropy in PythonBinary cross-entropy as a loss function Summary
Chapter 5: Simulation-Based Markov Decision Processes
Technical requirements Introducing agent-based modelsOverview of Markov processesThe agent-environment interfaceExploring MDPsUnderstanding the discounted cumulative reward Comparing exploration and exploitation conceptsIntroducing Markov chainsTransition matrixTransition diagramMarkov chain applicationsIntroducing random walksOne-dimensional random walkSimulating a 1D random walkSimulating a weather forecastBellman equation explainedDynamic programming concepts Principle of optimalityBellman equationMulti-agent simulationSchelling’s model of segregationPython Schelling model Summary
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 bootstrapApplying bootstrapping regressionExplaining permutation testsPerforming a permutation testApproaching cross-validation techniquesValidation 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 Exploring the gradient descent techniqueDefining descent methodsApproaching the gradient descent algorithmUnderstanding the learning rateExplaining the trial and error methodImplementing gradient descent in PythonUnderstanding the Newton-Raphson methodUsing the Newton-Raphson algorithm for root finding Approaching Newton-Raphson for numerical optimizationApplying the Newton-Raphson technique The secant methodDeepening our knowledge of stochastic gradient descentApproaching the EM algorithmEM algorithm for Gaussian mixture Understanding Simulated Annealing (SA)Iterative improvement algorithmsSA in actionDiscovering multivariate optimization methods in PythonThe Nelder-Mead methodPowell’s conjugate direction algorithmSummarizing other optimization methodologiesSummary
Chapter 8: Introducing Evolutionary Systems
Technical requirements Introducing SCFuzzy logic (FL)Artificial neural network (ANN)Evolutionary computationUnderstanding genetic programmingIntroducing the genetic algorithm (GA)The basics of GAGenetic operatorsApplying a GA for search and optimizationPerforming symbolic regression (SR)Exploring the CA modelGame-of-lifeWolfram code for CASummary
Part 3:Simulation Applications to Solve Real-World Problems
Chapter 9: 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 a time seriesIntroducing the Black-Scholes modelApplying the Monte Carlo simulationStudying risk models for portfolio managementUsing variance as a risk measureIntroducing the Value-at-Risk metricEstimating VaR for some NASDAQ assetsSummary
Chapter 10: 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 modelApproaching deep neural networksGetting familiar with convolutional neural networksExamining recurrent neural networksAnalyzing long short-term memory networksExploring GNNsIntroducing graph theoryAdjacency matrixGNNsSimulation modeling using neural network techniquesConcrete quality prediction model Summary
Chapter 11: 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 a fire startingScheduling project time using the Monte Carlo simulationDefining the scheduling gridEstimating the task’s timeDeveloping an algorithm for project schedulingExploring triangular distributionSummary
Chapter 12: Simulating Models for Fault Diagnosis in Dynamic Systems
Technical requirements Introducing fault diagnosisUnderstanding fault diagnosis methodsThe machine-learning-based approach Fault diagnosis model for a motor gearboxFault diagnosis system for an unmanned aerial vehicleSummary
Chapter 13: 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 models to real lifeModeling in healthcareModeling in financial applicationsModeling physical phenomenonModeling fault diagnosis systemModeling public transportationModeling human behaviorNext steps for simulation modelingIncreasing the computational powerMachine-learning-based models Automated generation of simulation modelsSummary
Index
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Content preview from Hands-On Simulation Modeling with Python - Second Edition

2 Understanding Randomness and Random Numbers

Frequently, in real life, it is useful to flip a coin in order to decide what to do. Many computers also use this procedure as part of their decision-making process. In fact, many problems can be solved in a very effective and relatively simple way by using probabilistic algorithms. In an algorithm of this type, decisions are made based on random contributions that remember the dice roll with the help of a randomly chosen value.

The generation of random numbers has ancient roots, but only recently has the process been speeded up, allowing it to be used on a large scale in scientific research as well. These generators are mainly used for computer simulations, statistical sampling techniques, or in ...

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Hands-On Simulation Modeling with Python - Second Edition

by Giuseppe Ciaburro

2

Understanding Randomness and Random Numbers

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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