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

Understanding Bayes' theorem

From the Bayesian point of view, probability measures the degree of likelihood that an event will occur. It is an inverse probability in the sense that from the observed frequencies, we obtain the probability. Bayesian statistics foresee the calculation of the probability of a certain event before carrying out the experiment; this calculation is made based on previous considerations. Using Bayes' theorem, by using the observed frequencies, we can calculate the a priori probability, and from this, we can determine the posterior probability. By adopting this method, the prediction of the degree of credibility of a given hypothesis is used before observing the data, which is then used to calculate the probability after observing the data.

Important Note

In the frequentist approach, we determine how often the observation falls in a certain interval, while in the Bayesian approach, the probability of truth is directly attributable to the interval...