Hands/On Simulation Modeling with Python
更新时间：2021-04-09 23:18:30
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Hands-On Simulation Modeling with Python
Hands-On Simulation Modeling with Python
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Contributors
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Preface
Who this book is for
What this book covers
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Section 1: Getting Started with Numerical Simulation
Chapter 1 Introducing Simulation Models
Introducing simulation models
Classifying simulation models
Approaching a simulation-based problem
Dynamical systems modeling
Summary
Chapter 2 Understanding Randomness and Random Numbers
Technical requirements
Stochastic processes
Random number simulation
The pseudorandom number generator
Testing uniform distribution
Exploring generic methods for random distributions
Random number generation using Python
Summary
Chapter 3 Probability and Data Generation Processes
Technical requirements
Explaining probability concepts
Understanding Bayes' theorem
Exploring probability distributions
Summary
Section 2: Simulation Modeling Algorithms and Techniques
Chapter 4 Exploring Monte Carlo Simulations
Technical requirements
Introducing Monte Carlo simulation
Understanding the central limit theorem
Applying Monte Carlo simulation
Performing numerical integration using Monte Carlo
Summary
Chapter 5 Simulation-Based Markov Decision Processes
Technical requirements
Overview of Markov processes
Introducing Markov chains
Markov chain applications
The Bellman equation explained
Multi-agent simulation
Summary
Chapter 6：Resampling Methods
Technical requirements
Introducing resampling methods
Exploring the Jackknife technique
Demystifying bootstrapping
Explaining permutation tests
Approaching cross-validation techniques
Summary
Chapter 7：Using Simulation to Improve and Optimize Systems
Technical requirements
Introducing numerical optimization techniques
Facing the Newton-Raphson method
Deepening our knowledge of stochastic gradient descent
Discovering the multivariate optimization methods in Python
Summary
Section 3: Real-World Applications
Chapter 8：Using Simulation Models for Financial Engineering
Technical requirements
Understanding the geometric Brownian motion model
Using Monte Carlo methods for stock price prediction
Studying risk models for portfolio management
Summary
Chapter 9：Simulating Physical Phenomena Using Neural Networks
Technical requirements
Introducing the basics of neural networks
Understanding feedforward neural networks
Simulating airfoil self-noise using ANNs
Exploring deep neural networks
Summary
Chapter 10：Modeling and Simulation for Project Management
Technical requirements
Introducing project management
Managing a tiny forest problem
Scheduling project time using Monte Carlo simulation
Summary
Chapter 11：What's Next?
Summarizing simulation modeling concepts
Applying simulation model to real life
Next steps for simulation modeling
Summary
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更新时间：2021-04-09 23:18:30