Book Image

Hands-On Simulation Modeling with Python

By : Giuseppe Ciaburro
Book Image

Hands-On Simulation Modeling with Python

By: Giuseppe Ciaburro

Overview of this book

Simulation modeling helps you to create digital prototypes of physical models to analyze how they work and predict their performance in the real world. With this comprehensive guide, you'll understand various computational statistical simulations using Python. Starting with the fundamentals of simulation modeling, you'll understand concepts such as randomness and explore data generating processes, resampling methods, and bootstrapping techniques. You'll then cover key algorithms such as Monte Carlo simulations and Markov decision processes, which are used to develop numerical simulation models, and discover how they can be used to solve real-world problems. As you advance, you'll develop simulation models to help you get accurate results and enhance decision-making processes. Using optimization techniques, you'll learn to modify the performance of a model to improve results and make optimal use of resources. The book will guide you in creating a digital prototype using practical use cases for financial engineering, prototyping project management to improve planning, and simulating physical phenomena using neural networks. By the end of this book, you'll have learned how to construct and deploy simulation models of your own to overcome real-world challenges.
Table of Contents (16 chapters)
Section 1: Getting Started with Numerical Simulation
Section 2: Simulation Modeling Algorithms and Techniques
Section 3: Real-World Applications

Exploring probability distributions

A probability distribution is a mathematical model that links the values of a variable to the probabilities that these values can be observed. Probability distributions are used to model the behavior of a phenomenon of interest in relation to the reference population, or to all the cases of which the researcher observes a given sample.

Based on the measurement scale of the variable of interest X, we can distinguish two types of probability distributions:

  • Continuous distributions: The variable is expressed on a continuous scale
  • Discrete distributions: The variable is measured with integer numerical values

In this context, the variable of interest is seen as a random variable whose probability law expresses the degree of uncertainty with which its values can be observed. Probability distributions are expressed by a mathematical law called probability density function (f(x)) or probability function (p(x)) for continuous or discrete...