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Course Information

Course Name
Turkish İleri Markov Sistemlerin Modelleme ve Benzetimi
English Advanced Modeling & Simulation of Markovian Sytems
Course Code
BLU 528E Credit Lecture
(hour/week)
Recitation
(hour/week)
Laboratory
(hour/week)
Semester -
3 3 - -
Course Language English
Course Coordinator Mehmet Akif Yazıcı
Course Objectives 1) To give an idea of Markov modeling through Memoryless and Markov properties
2) To teach pseudo-random number generation techniques
3) To teach how to understand and simulate various Markovian queueing systems
4) To teach how to write an efficient simulator
5) To give an idea of modeling and simulation methods in IT systems
Course Description Review of Probability Theory and Random Processes, Memoryless Property, Markov Property, Poisson Process, Pseudo-random Number Generation, Generation of Poisson Arrival Events, Simulation optimization, Confidence intervals, Simulation and performance measures of M/M/1, M/M/k, M/D/1, M/M/k/k, M/M/k/k/N systems, Erlang loss formulas, Queueing disciplines, Impatience Models, Batch Arrivals, Markovian Arrival Process (MAP), Phase-type service times, Aloha systems, Internet traffic, Router/switch simulation
Course Outcomes Students who pass the course will have knowledge about:
1) Memorylessness and Markov properties of random variables and processes,
2) Pseudo-random number generation techniques,
3) Characteristics of various Markovian queueing systems,
4) Efficient simulation of Markovian systems
5) Simulation of some real systems encountered in IT systems
Pre-requisite(s) There is no specific prerequisite course, but a fair level of probability theory and computer programming is expected.
Required Facilities
Other
Textbook
Other References [1] Sheldon. M. Ross. Simulation. Academic Press, 5th edition, December 2012
[2] William J. Stewart. Probability, Markov Chains, Queues, and Simulation: The Mathematical Basis of Performance Modeling. Princeton University Press, 2009
[3] Steven M. Kay, Intuitive Probability and Random Processes using MATLAB. Kluwer Academic Publishing, 2006
[4] Carl Graham, Denis Talay. Stochastic Simulation and Monte Carlo Methods: Mathematical Foundations of Stochastic Simulation. Springer-Verlag, 2013
[5] Jerry Banks, John S. Carson II, Barry L. Nelson, David M. Nicol. Discrete-Event System Simulation. Pearson, 5th edition, July 2009
 
 
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