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:

Assessable Learning Goals covered for Quiz 3

Know Fisher-Yates shuffling algorithm, be able to apply it to sampling tasks in simulations.

Know the reservoir sampling algorithm 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.

What is a distribution's idf()?

Know what constitutes a histogram and how it is connected to CDFs.

Know the inversion technique for non-stationary arrival times.

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 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.

Know what the construction technique means for random variates (of which summation is one example).

What does the parameter u in random variates represent? Computationally, how do we get a value for u in code?

Know the criteria by which variates are judged: portability, efficiency, clarity, syncronicity, ...

Know the two techniques for truncating pre-existing random variates.