Project (Term Project)
Extensive Problem Solving
Summary of Course Content
I. Brief review of prerequisite material in probability (0.5 weeks).
II. Discrete event simulation, using either the 'simmer' (R) or SimPy
(Python) languages (2 weeks).
III. Discrete and continuous-time Markov chains (1.5 weeks).
IV. Queuing models (1.5 weeks).
V. Application of Markov models to classification techniques:
Hidden Markov models (1 week).
VI. Application of Markov models to Bayesian methods:
Markov Chain Monte Carlo (1 week).
VII. Specific computer science applications, case studies (2.5 weeks).
Instructor's materials. Online textbook can be found: http://heather.cs.ucdavis.edu/probstatbook
Potential Course Overlap
This course does not have a significant overlap with any other course. It covers some topics similar to some in course 271, but with a more statistical view. There is also some topic similarity to MAT 135B, but with a much more applied emphasis. The major theme of this course, running throughout Topics II-VI above, is Markov models, applied in computer science contexts. There are no courses at all in the Department of Statistics on Markov modeling, MAT 135B does cover Topic III, but in a much more theoretical manner and not with computer science applications. Topic V has some overlap with course 271, but with a more statistical view. Topic V also has some overlap with STA 208, but again uniquely motivated by Markov models.
Note: This course originally was titled Probabilistic Modeling of Computer Systems. At some point, it was re-titled Performance Evaluation, but has always been taught in the original form, involving Markov models and applications to queuing analysis. The current change would revert to (close to) the original title, with a modernized topic list, notably adding Hidden Markov Models, Markov Chain Monte Carlo, and discrete event simulation software.
Yes Final Exam
Justification for No Final Exam
Grading: Letter; homework (50%), project (25%); final exam (25%) The project will include the design and analysis of a computer and/or communication system using the analytical and simulation methodologies developed in this course.