Developers working with the simulation models will be able to put their knowledge to work with this practical guide. You will work with real-world data to uncover various patterns used in complex systems using Python. The book provides a hands-on approach to implementation and associated methodologies to improve or optimize systems.

• 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
• Other Books You May Enjoy
  • Leave a review - let other readers know what you think
• Index