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 Final Exam Learning Goals
Know how to use accept/reject techniques for uniformly random (geometric) point generation.
Know how to write Monte Carlo simulations for estimating the Pr(A) of an event A.
What unique characteristic of a system or simulation makes it Monte Carlo?
What does the pRNG API routine Random() provide to a simulation writer?
What is a seed for a pRNG, how is it related to the sequence of valued generated by Random()?
What is ρ (rho) for pRNGs? When does the sequence of values from Random() repeat?
What are consistency checks? How can they be used in V&V?
What are the authors' five phases of simulation development?
What is simulation validation?
What is simulation verification?
What is the computational model of a simulation?
What is the conceptual model of a simulation?
What is the specification model of a simulation?
Understand the Simple Inventory System (SIS), it's assumptions and simplifications.
Know how the expected behavior or performance of an SSQ changes with varying levels of traffic intensity.
Know how to calculate traffic intensity and its connection to service rate.
Understand how a FIFO SSQ simulation can be written in a simple while loop and how ai and si can be manipulated for simple experiments.
Understand the canonical SSQ and appreciate its broad application to computer simulation.
Know the Equilikely(a,b) random variate: the meaning of its parameters, pmf, and CDF.
Know the F(x) inversion technique for constructing random variates. What is the requirement on F(x)?
Know the Uniform(a,b) random variate: the meaning of its parameters, pdf, and CDF.
Understand the problems with the often used and always flawed RandomInteger() mod SIZE programming pattern.
What does the parameter u in random variates represent? Computationally, how do we get a value for u in code?