Hands-On Simulation Modeling with Python: Develop simulation models to get accurate results and enhance decision-making processes

Cover
Title Page
Copyright and Credits
About Packt
Contributors
Table of Contents
Preface
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
	Summary
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