Due Dates
Unless otherwise specified, Learning Group Assignments should be completed for the next lecture day.
Semester Calendar
| Date | Lecture Title | Assignment(s) | Lecture & Assignment Goals |
|---|---|---|---|
| Notes | |||
| Wed Dec 10 |
Classes End |
||
| Tue Dec 9 | Quiz 3 | ||
| Wed Nov 26 – Fri Nov 28 |
Thanksgiving Holiday - no lecture |
||
| Fri Nov 14 |
Last day to withdraw (no refund) |
||
| Thu Oct 30 | Quiz 2 | ||
| Mon Oct 20 – Tue Oct 21 |
Fall Break |
||
| Thu Oct 16 | Midterm Exam | Here are the learning goals for the exam. |
|
| Tue Oct 14 | Simulation Correlations | LG Assignment: |
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?
What is the difference between serial and paired correlation?
Given Welford's covariance equations (Thm 4.4.3), be able to apply them to a set of paired data (u,v).
What is autocorrelation and autocorrelation lag? Why is autocorrelation important in DES?
What is positive (and negative) serial correlation? Cite a positive serial correlation example from the SSQ model.
Know what constitutes a density histogram and how it is connected to PDFs.
How is the height of a continuous histogram bin calculated? 
How is the mean and standard deviation of a continuous data histogram differ than the underlying dataset?
What is a mean-square orthogonal-distance (MSOD) linear regression line?
Why are MSOD regression lines more suitable for DES?
Why don't we use standard statistical linear regression lines for DES results?
Know that valid computer simulations do not produce outliers. End of discussion. |
|
Lecture slides for the square root rule. Lecture slides on linear correlation. |
|||
| Thu Oct 9 | Histograms |
What are empirical CDFs? How are they better than histograms for assesing a data distribution?
Know what constitutes a density histogram and how it is connected to PDFs.
Know the general approach to binning continuous data, about how many bins are needed for a sample of
How is the mean and standard deviation of a continuous data histogram differ than the underlying dataset?
Why would we ever need to calculate mean and std dev from histogram data?
Know that valid computer simulations do not produce outliers. End of discussion.
Understand the problems with the often used and always flawed |
|
|
Lecture slides on histograms Lecture slides for a simply flawed Monte Carlo experiment. |
|||
| Tue Oct 7 | Simulation Stats |
LG Assignment:
Individual Assignment: Students are encouraged to browse through chapter 4 of the textbook. A good chunk of it will be hopefully review. Some of the LGA assignments or questions will have specific reading responsibilities. Use the associated learning goals to guide your reading! The more important sections of chapter 4 to read through are 4.1.3 Examples which discusses the problems created by non-independent samples and 4.4.2 Serial Correlation. This reading is needed because the topics were (likely) not dealt with (or not discussed enough) in introductory statistics courses. |
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.
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.
Implement Welford's Equations for mean and variance in your preferred language for future course projects.
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 valid computer simulations do not produce outliers. End of discussion. |
|
LGA discussion slides for uniform arrivals vs uniform interarrivals. Lecture slides for Welford's Equations, and the derivation of Welford's discrete forms of the average and ivi. Derivations for continuous forms follow the same pattern, with only a couple more curves in the road |
|||
| Thu Oct 2 | Uniform Arrivals | LG Assignment: |
Know that uniform arrival times and uniform interarrivals are not the same thing.
Understand the steps in going from uniform arrival times to exponential interarrival times.
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 Geometric(p) random variate, it's parameter p, and it's connection to Exponential(mu) |
|
Exponential and Geometric distributions and variates |
|||
| Tue Sep 30 | Quiz 1 |
LG Assignment:
There is no LGA due for the next lecture. Individual Assignment: War-and-Trash (due Wed 22 by 11:59PM) |
Understand the game-play of the two card games in project War-and-Trash (due Wed 22 by 11:59PM). |
|
Here are the learning goals for the first learning group quiz. After the quiz is graded, you will be assigned to your second round learning groups. |
|||
| Tue Sep 30 | |||
| Thu Sep 25 | Project 1 |
LG Assignment:
Individual Assignment: War-and-Trash (due Wed 22 by 11:59PM) |
Understand the problems with the often used and always flawed
Understand the game-play of the two card games in project War-and-Trash (due Wed 22 by 11:59PM). |
|
Lecture slides for a simply flawed Monte Carlo experiment. |
|||
| Tue Sep 23 | Random Points |
LG Assignment:
Individual Assignment: Read about project requirements for the course; we'll review these (briefly) next lecture. Here is the write-up for your first programming project (due Wed 22 by 11:59PM), we'll review this Thursday before the quiz. |
Know how to use accept/reject techniques for uniformly random (geometric) point generation.
Know the pitfalls associated with common (naive) methods of random point generation.
What characteristics of random points in a two dimensional space suggest a flawed algorithm was used?
Be able to analyze and critique algorithms for generating random points in a 2d plane.
Know at least one non accept/reject technique for randomizing points within an arbitrary triangle.
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()?
Understand the submission and I/O requirements of simuation projects for the course. |
|
Lecture slides for random points. |
|||
| Thu Sep 18 | Probability Primer & Monte Carlo Simulations | LG Assignment: |
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?
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 are replications in the context of Monte Carlo simulations?
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
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. |
|
chl_cycle-vs-random-results.pdf Lecture slides for Monte Carlo simulations. Some of you may be missing §2.3 in your text, this might help. |
|||
| Tue Sep 16 |
Career Days - No Lecture |
||
| Thu Sep 11 | Simple Inventory System (SIS) | LG Assignment: |
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?
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?
How did back-ordering inventory manifest itself in the SiS conceptual, specification, and computational models?
How did flow-balanced inventory manifest itself in the SiS conceptual, specification, and computational models?
How did zero delivery lag manifest itself in the SiS conceptual, specification, and computational models?
In the SiS case study, what were s and S (note the case) and how did the simulation experiment vary one or both of them?
What SiS assumption about demand over an inventory review period simplified the specification model? |
|
Considering q-bar in questions 1.2.2 and 1.2.8 group question. Here is the Code base for LGA on SIS. Lecture slides for simple inventory systems. |
|||
| Wed Sep 10 |
Census day |
||
| Tue Sep 9 | Introduction to SiS |
LG Assignment:
No LGA assigned, don't get used to it! |
|
|
Considering q-bar in questions 1.2.2 and 1.2.8 group question. |
|||
| Thu Sep 4 | Coding SSQs | LG Assignment: |
What is simulation validation?
What is simulation verification?
Name two acceptable ways to validate a simulation.
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 algebraic form of Little's equations connecting the two types of statistics.
What properties must an SSQ have in order to apply Little's Equations to its statistical measures.
How were arrival times and service times "modeled" in the Sven & Larry case study?
Is the relationship between traffic intensity and average queue length linear or non-linear?
Know how to use an appropriate indicator function in the proof of Little's Theorem.
Know the "pattern of the proof" using indicator functions to show Little's equations for SSQs.
Which variable was altered Sven & Larry ice cream parlor simulation experiment? How was it altered? Was the measure of traffic intensity affected? |
|
Considering V&V group question. Lecture slides for Little's Equations and Traffic Intensity. Here is the tarball with the provided ssq programs. |
|||
| Tue Sep 2 | Introduction to SSQs | LG Assignment: |
Know the rules for presenting discrete and continuous data relationships (scatter plots vs connected dots).
Know how to calculate traffic intensity and its connection to service rate.
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 algebraic form of Little's equations connecting the two types of statistics.
Know the four different types of queuing disciplines that might be used in an SSQ simulation.
Which of the SSQ time measures (there are 6) are timestamps and which are time intervals? |
| Mon Sep 1 |
Labor Day Holiday - Campus Closed |
||
| Thu Aug 28 | Simulation Design | LG Assignment: |
Learn about several different simulation approaches, topics, and uses.
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? |
|
Intro to Simulation Design slides. |
|||
| Tue Aug 26 | This is merely a simulation |
Individual Assignment:
Review the course syllabus and read how Learning Groups with participation points will be used in the course. You may also want to look through the assignment submission guidelines so you know what you're getting into. From this zip, read one paper (choose the one of most interest to you), and be prepared to discuss it in the next lecture. |
Learn of several different simulations reported in the literature, compare and constrast their pros and cons.
Understand how the course participation grade will be factored into your course grade.
Understand how your course grade will be calculated. |
|
Students on Monday requested to see an example of what a SIM project write-up looks like. Here are two that I'm pretty sure we won't be doing this semester: You do not (yet) need a login for the course website, when the time comes you will authenticate through Mine's SSO or individualized logins (TBD). This course will not use Canvas or Blackboard. |
|||
| Mon Aug 25 |
Classes Start |
||