Computer Simulation: A Foundational Approach Using Python

Description

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. Typically, simulation involves two key steps: modeling and implementation. In this book, these processes are divided to minimize confusion and help introductory students grasp the transition to stochastic simulation. Modeling is covered in the first half of each chapter and is presented with event graphs and design of experiments. Then, as a tool of choice for scientists and engineers, Python programs are used throughout the book to help students implement their knowledge. The chapters are aided by a heavy use of Illustrations, and many examples are provided to re-enforce the concepts presented. The book also features a full chapter of case studies, as well as real working code, encouraging students to compare results obtained from simulation with those obtained using analytical methods. Additionally, most of the necessary mathematics is abstracted by procedures that show how the mathematical techniques can be implemented in the real world. This helps make the shift from models to programs straightforward. Some other features of this book are: Simulation programs are divided into two distinct components: simulator and model. Both simulation without an event list and with an event list are covered. The single-queue single-server queuing system is used as a running example throughout the book A complete chapter on the Monte Carlo method and variance-reduction techniques are included. 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. Content: PART I THE FUNDAMENTALS1 Introduction The Pillars of Science And Engineering Studying the Queueing Phenomenon What Is Simulation? Lifecycle of a Simulation Study Advantages and Limitations of SimulationOverview of the Book2 Building Conceptual ModelsWhat Is a Conceptual Model? Elements of a Conceptual Model The Singleserver Queueing SystemState Diagrams Actual Time Versus Simulated Time 3 Simulating Probabilities Random Experiments and Events What Is Probability? Computing Probabilities Probability as a Sample Mean 4 Simulating Random Variables and Stochastic ProcessesWhat Are Random Variables? Some Useful Random Variables Stochastic Processes Dynamic System Evolution Simulating Queueing Processes 5 Simulating the SingleServer Queueing System Simulation Model Collecting Simulated Data Performance Laws Independent Simulation Runs Transient And Steady Phases 6 Statistical Analysis of Simulated Data Populations and Samples Probability Distribution Of The Sample Mean Confidence Intervals Comparing Two System Designs PART II MANAGING COMPLEXITY7 Event Graphs What Is an Event Graph? Examples Translating Event Graphs Into Code 8 Building Simulation Programs Timedriven Simulation Eventdriven Simulation Writing Eventdriven Simulation Programs Programming Issues PART III PROBLEM-SOLVING9 The Monte Carlo Method Estimating the Value of Numerical Integration Estimating a Probability Variance Reduction Techniques PART IV SOURCES OF RANDOMNESS10 Random Variate Generation The Inversion Method The Rejection Method The Composition Method The Convolution Method Specialized Methods 11 Random Number Generation Pseudorandom Numbers Characteristics of a Good Generator Just Enough Number Thoery The Linear Congruential Method The Multiplicative Congruential Method Linear Feedback Shift Registers Statistical Testing of Rngs PART V CASE STUDIES12 Case Studies Network Reliability Packet Delivery Over a Wireless Channel Simple Arq Protocol Appendix A Overview of Python Appendix B An ObjectOrientedAppendix C The ChiSquaredAppendix D The Distribution