CS 1538 Fall 2009 Online Syllabus
Note: The information in this document is subject to frequent change. It is in your best interest to check it frequently throughout the term.
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Class # |
Date |
Topic(s) |
References |
Comments/Handouts |
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1 |
9/1 |
Course Introduction and Policies; Intro. to Simulation; Some Simulation Definitions |
Handout; Banks[1] Chapter 1 |
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2 |
9/3 |
More Simulation
Definitions and Ter |
Banks Chapters 2, 3 |
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3 |
9/8 |
Simple simulation example (Grocery Store) using a spreadsheet Discussion in detail of programming the Simple Grocery Store example; Some simulation tools (Queues and Priority Queues); more simulation principles |
Banks Chapters 2,3 |
GrocerySim.java, SimEvent.java, |
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4 |
9/10 |
Finish Grocery Simulation example Newspaper Seller's Example |
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5 |
9/15 |
Finish Newspaper Seller's Example Intro. to Mathematical and Statistical Models (Discrete random variables, expected value, variance, pdf and cdf) |
Banks Section 5.1 |
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6 |
9/17 |
Discrete distributions: Bernoulli Trials; Binomial Distribution; Geometric Distribution; Proof that Geometric Distribution is memoryless; Poisson Distribution Continuous random variables: pdf, cdf, expected value, variance |
Banks Section 5.3 Banks Sections 5.1, 5.4 |
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7 |
9/22 |
Continuous distributions: uniform distribution; exponential distribution; gamma distribution; normal distribution |
Banks Section 5.4 |
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8 |
9/24 |
Finish normal distribution; Poisson Arrival process Intro. to Monte Carlo Simulation: simple example and definitions |
Course Notes |
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9 |
9/29 |
Integration via Monte Carlo methods; Simulated Annealing Intro to Queueing Theory – basic notation |
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10 |
10/1 |
More queueing theory definitions; long-run measures (ex: L, LQ, etc); server utilization; steady-state systems |
Banks Sections 6.1-6.3 |
GrocerySimB.java SimEventFloat.java ArrivalEventFloat.java CompletionEventFloat.java |
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11 |
10/6 |
Analytical determination of some long-run measures for Markovian systems; do some example problems |
Banks Sections 6.4-6.5 |
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12 |
10/8 |
More queueing example problems "Lite Theory" on analytical formulas for Markovian systems Intro to Random numbers and pseudo-random number generation; linear-congruential generators |
See notes Banks Sections 7.1-7.3 Knuth[2] Sections 3.1-3.2 |
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13 |
10/15 |
More on random number generation; testing uniformity using Chi-Square MIDTERM MATERIAL |
Banks Section 7.4 Knuth Section 3.3.1 IS ABOVE |
THIS POINT |
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14 |
10/20 |
Kolmogorov-Smirnov test Review questions for exam |
Banks Section 7.4 |
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15 |
10/22 |
MIDTERM |
EXAM |
GIVEN |
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16 |
10/27 |
Finish uniformity tests; tests for independence |
Banks 3rd Edition Handout; Knuth Handouts |
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17 |
10/29 |
Other random number generators Random variates |
Banks Section 7.3 Knuth Section 3.3.2 Banks Chapter 8 |
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18 |
11/3 |
In class extra credit exercise – Max of T test |
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Solution: MaxT.java |
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19 |
11/5 |
Finish random variates Start input modeling – get data from Panera |
Banks Chapter 9 |
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20 |
11/10 |
Input modeling – processing data from Panera, goodness of fit tests (Chi-Square, Chi-Square with equal probabilities |
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21 |
11/12 |
More input modeling – p-values, covariance and correlation, time-series input models |
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22 |
11/17 |
Verification and validation – comparing generated and measured data Discussion of Project 3 |
Banks Chapter 10 |
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23 |
11/19 |
Brief review Beta error and the “power of the test”, reducing beta errors |
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24 |
11/24 |
Output analysis for a single model – transient behavior vs. steady-state behavior, considering variable of interest across runs (to allow independence), confidence intervals for a given number of runs |
Banks Chapter 11 |
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25 |
12/1 |
Quantiles and percentiles Steady-State Analysis; using batches to improve convergence to steady-state |
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26 |
12/3 |
Comparing 2 Designs with independent data and with correlated data Project update |
Banks Chapter 12 |
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27 |
12/8 |
Choosing the “best” in a system |
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28 |
12/10 |
Misc. Chapter 12 topics Review for Final Exam |
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