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Hands-On Simulation Modeling with Python Develop Simulation Models for Improved Efficiency and Precision in the Decision-Making Process /
Learn to construct state-of-the-art simulation models with Python and enhance your simulation modelling skills, as well as create and analyze digital prototypes of physical models with ease Key Features Understand various statistical and physical simulations to improve systems using Python Learn to...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing, Limited,
2022.
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| Edición: | 2nd ed. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
Tabla de Contenidos:
- Cover
- Title Page
- Copyright and Credits
- Dedication
- Contributors
- Table of Contents
- Preface
- 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