Computer Simulation

This book introduces the fundamentals of computer simulation using Python. Monte Carlo methods and applications are covered extensively, along with advanced topics, such as event graphs and the design of experiments.

Computer simulation is an effective and popular universal tool that can be applied to almost all disciplines. Requiring only basic knowledge of programming, mathematics, and probability theory, Computer Simulation: A Foundational Approach Using Python takes a hands-on approach to programming to introduce the fundamentals of computer simulation.

The main target of the book is computer science and engineering students who are interested mainly in directly applying the techniques to their research problems. The book will be of great interest to senior undergraduate and starting graduate students in the fields of computer science and engineering and industrial engineering.

Part I The Fundamentals. Chapter 1 Introduction. Chapter 2 Building Conceptual Models. Chapter 3 Simulating Probabilities. Chapter 4 Simulating Random Variables and Stochastic Processes. Chapter 5 Simulating the SingleServer Queueing System. Chapter 6 Statistical Analysis of Simulated Data. Part II Managing Complexity. Chapter 7 Event Graphs. Chapter 8 Building Simulation Programs. Part III Problem-Solving. Chapter 9 The Monte Carlo Method. Part IV Sources Of Randomness. Chapter 10 Random Variate Generation. Chapter 11 Random Number Generation. Part V Case Studies. Chapter 12 Case Studies