Hands-On Simulation Modeling with Python: Develop simulation models to help you get accurate results and enhance the decision-making process, 2nd Edition

Hands-On Simulation Modeling with Python
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Preface
    Who this book is for
    What this book covers
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Part 1:Getting Started with Numerical Simulation
Chapter 1: Introducing Simulation Models
    Technical requirements
    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
    Exploring Discrete Event Simulation (DES)
        Finite-state machine (FSM)
        State transition table (STT)
        State transition graph (STG)
    Dynamic systems modeling
        Managing workshop machinery
        Simple harmonic oscillator
        The predator-prey model
    How to run efficient simulations to analyze real-world systems
    Summary
Chapter 2: Understanding Randomness and Random Numbers
    Technical requirements
    Stochastic processes
        Types of stochastic processes
        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
        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
        Generating real-value distributions
    Randomness requirements for security
        Password-based authentication systems
        Random password generator
    Cryptographic random number generator
        Introducing cryptography
        Randomness and cryptography
        Encrypted/decrypted message generator
    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
        The probability density function
        Mean and variance
        Uniform distribution
        Binomial distribution
        Normal distribution
    Generating synthetic data
        Real data versus artificial data
        Synthetic data generation methods
    Data generation with Keras
        Data augmentation
    Simulation of power analysis
        The power of a statistical test
        Power analysis
    Summary
Part 2:Simulation Modeling Algorithms and Techniques
Chapter 4: Exploring Monte Carlo Simulations
    Technical requirements
    Introducing the 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
        The central limit theorem
    Applying the Monte Carlo simulation
        Generating probability distributions
        Numerical optimization
        Project management
    Performing numerical integration using Monte Carlo
        Defining the problem
        Numerical solution
        Min-max detection
        The Monte Carlo method
        Visual representation
    Exploring sensitivity analysis concepts
        Local and global approaches
        Sensitivity analysis methods
        Sensitivity analysis in action
    Explaining the cross-entropy method
        Introducing cross-entropy
        Cross-entropy in Python
        Binary cross-entropy as a loss function
    Summary
Chapter 5: Simulation-Based Markov Decision Processes
    Technical requirements
    Introducing agent-based models
    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
        One-dimensional random walk
        Simulating a 1D random walk
        Simulating a weather forecast
    Bellman equation explained
        Dynamic programming concepts
        Principle of optimality
        Bellman equation
    Multi-agent simulation
    Schelling’s model of segregation
        Python Schelling model
    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
    Applying bootstrapping regression
    Explaining permutation tests
    Performing a permutation test
    Approaching cross-validation techniques
        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
    Exploring the gradient descent technique
        Defining descent methods
        Approaching the gradient descent algorithm
        Understanding the learning rate
        Explaining the trial and error method
        Implementing gradient descent in Python
    Understanding the Newton-Raphson method
        Using the Newton-Raphson algorithm for root finding
        Approaching Newton-Raphson for numerical optimization
        Applying the Newton-Raphson technique
        The secant method
    Deepening our knowledge of stochastic gradient descent
    Approaching the EM algorithm
        EM algorithm for Gaussian mixture
    Understanding Simulated Annealing (SA)
        Iterative improvement algorithms
        SA in action
    Discovering multivariate optimization methods in Python
        The Nelder-Mead method
        Powell’s conjugate direction algorithm
        Summarizing other optimization methodologies
    Summary
Chapter 8: Introducing Evolutionary Systems
    Technical requirements
    Introducing SC
        Fuzzy logic (FL)
        Artificial neural network (ANN)
        Evolutionary computation
    Understanding genetic programming
        Introducing the genetic algorithm (GA)
        The basics of GA
        Genetic operators
    Applying a GA for search and optimization
    Performing symbolic regression (SR)
    Exploring the CA model
        Game-of-life
        Wolfram code for CA
    Summary
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 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 a time series
        Introducing the Black-Scholes model
        Applying the Monte Carlo simulation
    Studying risk models for portfolio management
        Using variance as a risk measure
        Introducing the Value-at-Risk metric
        Estimating VaR for some NASDAQ assets
    Summary
Chapter 10: 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
    Approaching deep neural networks
        Getting familiar with convolutional neural networks
        Examining recurrent neural networks
        Analyzing long short-term memory networks
    Exploring GNNs
        Introducing graph theory
        Adjacency matrix
        GNNs
    Simulation modeling using neural network techniques
        Concrete quality prediction model
    Summary
Chapter 11: 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 a fire starting
    Scheduling project time using the Monte Carlo simulation
        Defining the scheduling grid
        Estimating the task’s time
        Developing an algorithm for project scheduling
        Exploring triangular distribution
    Summary
Chapter 12: Simulating Models for Fault Diagnosis in Dynamic Systems
    Technical requirements
    Introducing fault diagnosis
        Understanding fault diagnosis methods
        The machine-learning-based approach
    Fault diagnosis model for a motor gearbox
    Fault diagnosis system for an unmanned aerial vehicle
    Summary
Chapter 13: 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 models to real life
        Modeling in healthcare
        Modeling in financial applications
        Modeling physical phenomenon
        Modeling fault diagnosis system
        Modeling public transportation
        Modeling human behavior
    Next steps for simulation modeling
        Increasing the computational power
        Machine-learning-based models
        Automated generation of simulation models
    Summary
Index
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