Computer Simulation Learning Goals Summary.pdf

10/7/24, 7:26 PM Computer Simulation Learning Goals Summary Computer Simulation The following is simply a conveniently formatted list of learning goals. More likely than not, some might be hard to assess through a quiz...

10/7/24, 7:26 PM Computer Simulation Learning Goals Summary Computer Simulation 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 Exam 1 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. Know the pitfalls associated with common (naive) methods of random point generation. What unique characteristic of a system or simulation makes it Monte Carlo? Know the tell-tale feature(s) of spatial plots produced by faulty point generation algorithms. When randomizing points in a circle without accept/reject, how should the radi r be chosen using Random()? Why is it important to use multiple seeds and many replications in Monte Carlo simulations. 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? Know how derivations between the CDF (F(x)) and the PDF (f(x)) of continuous valued distributions are performed. Know how to calculate the Pr(Event) for both discrete and continuous valued distributions. Know how to calculate the mean and standard deviation of discrete data. Understand why the traditional one-pass variance equation is flawed for use in computer simulation. What are empirical CDFs? How are they better than histograms for assesing a data distribution? Know what constitutes a histogram and how it is connected to CDFs. Know the general approach to binning continuous data, about how many bins are needed for a sample of n? 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. Know that Welford's Equations exist, why they are superior to the "one-pass" algorithm common in statistical texts. Know which of the two (non Welford) standard equations for calculating s2 or s is flawed. DES people don't need integrals and anti-differentiation when integrating sample paths. Why not? Know that uniform arrival times and uniform interarrivals are not the same thing. Know that valid computer simulations do not produce outliers. End of discussion. 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? Name two acceptable ways to validate a simulation. When can a particular phase (concept, specification, computational, v&v) of simulation development be skipped? https://cs.mcprogramming.com/djsim/dlg/learninggoals?end=2024-10-07&title=Exam 1&ltpri=2&droptopics=SIM/STAT/CORR 1/2 10/7/24, 7:26 PM Computer Simulation Learning Goals Summary Understand the Simple Inventory System (SIS), it's assumptions and simplifications. Understand the experimental design of the Simple Inventory System case study; how was an optimal s determined? 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. Be familar with the job averaged statistics and time averaged statistics of an SSQ. Know the four different types of queuing disciplines that might be used in an SSQ simulation. What properties must an SSQ have in order to apply Little's Equations to its statistical measures. Which of the SSQ time measures (there are 6) are timestamps and which are time intervals? Know the Equilikely(a,b) random variate: the meaning of its parameters, pmf, and CDF. Know the Exponential(mu) random variate and the interpretation of its parameter mu in the context of arrival times. 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? Know the difference between a random number and a random variate. https://cs.mcprogramming.com/djsim/dlg/learninggoals?end=2024-10-07&title=Exam 1&ltpri=2&droptopics=SIM/STAT/CORR 2/2