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

The Bellman equation explained

In 1953, Richard Bellman introduced the principles of dynamic programming in order to efficiently solve sequential decision problems. In this type of problem, decisions are periodically implemented and influence the size of the model. In turn, these influence future decisions. The principle of optimality, enunciated by Bellman, allows, through an intelligent application, you to efficiently deal with the complexity of the interaction between the decisions and the sizes of the model. Dynamic programming techniques were also applied from the outset to problems in which there is no temporal or sequential aspect.

Important Note

Although dynamic programming can be applied to a wide range of problems by providing a common abstract model, from a practical point of view, many problems require models of such dimensions to preclude, then as now, any computational approach. This inconvenience was then called the 'curse of dimensionality' and was an...