MS&E 323 |
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General Information
·Lectures,
Problem Sessions and Office Hours
Course
Description
Emphasis is on the theoretical foundations of
simulation methodology. Generation of uniform and non-uniform random variables.
Discrete-event simulation and generalized semi-Markov processes. Output analysis
(autoregressive, regenerative, spectral, and stationary times series methods).
Variance reduction techniques (antithetic variables, common random numbers,
control variables, discrete-time, conversion, importance sampling). Stochastic
optimization (likelihood ratio method, perturbation analysis, stochastic
approximation). Simulation in a parallel environment.
Please see Contact Info.
Lectures, Problem
Sessions and Office hours
Lectures:
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Tuesday
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3:15
PM to 4:30 PM
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Location: McCullough
122 |
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Thursday
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3:15
PM to 4:30 PM
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Location: McCullough
122 |
Office Hours:
Professor Glynn: Xiaowei Zhang:
Friday
3:15
Location: Terman 321
Prerequisites
MS&E 221 or equivalent
Required Text
Asmussen,
S. and Glynn, P Stochastic Simulation: Algorithms and Analysis. Springer-Verlag,
2007.
Other useful references
Rubinstein,
R.Y., Simulation and the Monte Carlo method. Wiley, 1981.
Ripley B.D., Stochastic
Simulation. Wiley, 1987.
Henderson, S.G. and
P.W. Glynn,Regenerative steady-state
simulation of discrete-event systems. ACM Transactions on Modeling and
Computer Simulation 11 313-345
Assignments
For assignments, collaboration
among students in the class is encouraged. You may discuss the problems
with others, but you must write up solutions independently. Please
include a small statement mentioning the names of the students with whom
you discussed the problems, whether you benefitted from help or comments
from the instructor or the CA, and whether you obtained something from
a text or other source.
Exams
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Final: |
Time: Wednesday, March 18, 12:15PM - 3:15 PM |
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Location: McCullough 122 |
Grading
The course grade will be based on assignments (40%), project
(20%), and final exam (40%).