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

Chapter 5: Simulation-Based Markov Decision Processes

Markov Decision Processes (MDPs) model decision-making in situations where outcomes are partly random and partly under the control of a decision maker. An MDP is a stochastic process characterized by five elements: decision epochs, states, actions, transition probability, and reward. The characteristic elements of a Markovian process are the states in which the system finds itself and the available actions that the decision maker can carry out on those states. These elements identify two sets: the set of states in which the system can be found and the set of actions available for each specific state. The action chosen by the decision maker determines a random response from the system, which brings it into a new state. This transition returns a reward that the decision maker can use to evaluate the goodness of their choice. In this chapter, we will learn how to deal with decision-making processes with Markov chains. We will analyze...