What This Document Is
This document is a set of lecture slides focused on the crucial topic of random variate generation within the field of computer simulation. It delves into the methods and considerations for accurately representing real-world randomness within simulation models. Specifically, it explores how to select appropriate probability distributions to model system components and behaviors, moving beyond simply generating uniform random numbers. The material appears to be geared towards an upper-level undergraduate or graduate course in computer simulation.
Why This Document Matters
Students taking courses in modeling and simulation, particularly those focused on discrete-event simulation, will find this material highly valuable. It’s essential for anyone needing to build realistic and reliable simulations. Understanding how to choose and implement appropriate random variates is fundamental to ensuring a simulation accurately reflects the system being modeled. This resource would be particularly useful when you are tasked with validating a simulation against real-world data or when you need to perform sensitivity analysis on different distributional assumptions.
Common Limitations or Challenges
This resource focuses on the *theory* and *considerations* behind random variate generation. It does not provide pre-built code libraries or detailed implementation instructions for specific simulation software packages. While it touches on practical application through examples, it doesn’t offer a comprehensive guide to coding these distributions from scratch. It also assumes a foundational understanding of basic statistical concepts like probability density functions and cumulative distribution functions.
What This Document Provides
* An overview of the problem of modeling random components within a simulation.
* Guidance on selecting appropriate probability distributions based on system characteristics.
* Discussion of both formal “goodness-of-fit” tests and informal methods for distribution selection.
* A review of key statistical concepts like Probability Density Functions (PDFs) and Cumulative Density Functions (CDFs).
* Illustrative examples of how to apply these concepts to real-world scenarios, such as modeling customer arrivals.
* Consideration of the relationship between empirical data and theoretical distributions.