Hands-on simulation modeling with Python : develop simulation models to get accurate results and enhance decision-making processes /

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 methodologi...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Ciaburro, Giuseppe (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Birmingham, UK : Packt Publishing, 2020.
Temas:
Python (Computer program language)
Computer simulation.
Simulation methods.
Decision making > Data processing.
Computer Simulation
Python (Langage de programmation)
Simulation par ordinateur.
Méthodes de simulation.
Prise de décision > Informatique.
simulation.
simulation methods.
Computer programming.
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • 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

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