The following is simply a conveniently formatted list of learning goals. More likely than not, some might be hard to assess through a quiz or exam. Students should prioritize their studying based on:
- how much time in lecture was spent discussing or understanding the topic,
- how much the topic was stressed in learning group assignments
- and the student's comfort level with the topic
Assessable Learning Goals covered for Quiz 2
Know Fisher-Yates shuffling algorithm, be able to apply it to sampling tasks in simulations.
Know the reservoir sampling algorithm (6.5.6) for selecting n random elements from an unkown sized population.
Know the several different properties and attributes of sampling and shuffling algorithms presented in the text.
Know how to write Monte Carlo simulations for estimating a discrete data histogram of many events.
Know how to write Monte Carlo simulations for estimating the Pr(A) of an event A.
Describe S, the system state. What does "system" refer to?
How is the simulation clock different than the wall clock or an accelerated, virtual clock?
Name four data elements or data collections that go into a next event simulation (this list may not be exhaustive).
Understand the basic steps in augmenting a pre-existing NES with additional system and meta events.
What is the difference between system events and meta events?
Practice understanding a pre-existing NES simulation, and making changes to it.
Know the square root rule, by increasing the replication size 9x, how much smaller will our confidence intervals be?
How does serial correlation affect estimated mean and confidence interval widths?
Know what constitutes a histogram and how it is connected to CDFs.
How is the height of a continuous histogram bin calculated?
What is a mean-square orthogonal-distance (MSOD) linear regression line?
Why are MSOD regression lines more suitable for DES?
Given Welford's discrete and integral mean and variance equations (Thms 4.1.2, 4.1.4), be able to apply them to a set of data.
DES people don't need integrals and anti-differentiation when integrating sample paths. Why not?
Know the relationships between PMF/PDF and CDF for both discrete and continuous distributions.
Know the relationships between PMF/PDF, CDF, and InvDF. How do you derive one from another? Can this always be done?
Know how to maniuplate and derive CDFs and PDFs of variables that are functions of a Uniform(a,b) input.
Know how to identify and create truncated probability distributions using an accept-reject technique.
Know the CDF search technique for creating discrete random variates.
Know the F(x) inversion technique for constructing random variates. What is the requirement on F(x)?
Know the three techniques for constructing a discrete random variate.
What does the parameter u in random variates represent? Computationally, how do we get a value for u in code?
Be able to recognize and apply the formulas of variate truncation by CDF modification.
Know the constrained inversion technique for truncated random variates.
Know the criteria by which variates are judged: portability, efficiency, clarity, syncronicity, ...
Know three techniques for truncating pre-existing random variates.