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E2 204 Jan 3:0 Stochastic Processes and Queueing Theory: Instructor

This document provides information about the course "Stochastic Processes and Queueing Theory" including the instructor's details, brief course description, prerequisites, syllabus, course outcomes, grading policy, and assignments. The course will introduce students to basic stochastic processes tools to model complex systems with uncertainty and analyze system performance. Topics include Poisson process, renewal theory, Markov chains, reversibility, queueing networks, and martingales. The grading will be based on assignments, mid-term, project, and final exam.

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Kurada Ravindra
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78 views2 pages

E2 204 Jan 3:0 Stochastic Processes and Queueing Theory: Instructor

This document provides information about the course "Stochastic Processes and Queueing Theory" including the instructor's details, brief course description, prerequisites, syllabus, course outcomes, grading policy, and assignments. The course will introduce students to basic stochastic processes tools to model complex systems with uncertainty and analyze system performance. Topics include Poisson process, renewal theory, Markov chains, reversibility, queueing networks, and martingales. The grading will be based on assignments, mid-term, project, and final exam.

Uploaded by

Kurada Ravindra
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
Available Formats
Download as PDF, TXT or read online on Scribd
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E2 204 Jan 3:0

Stochastic Processes and Queueing Theory

Instructor
Parimal Parag
Email: parimal@iisc.ac.in
Teaching Assistant

Email:

Department: ECE
Course Time:
Lecture venue:
Detailed Course Page: http://ece.iisc.ernet.in/~parimal/spqt.html

Announcements

Brief description of the course


Basic mathematical modeling is at the heart of engineering. In both electrical and computer engineering, many

complex systems are modeled using stochastic processes. This course will introduce students to basic

stochastic processes tools that can be utilized for performance analysis and stochastic modeling.
Prerequisites
First graduate engineering course in probability theory and random variables.
Syllabus
Poisson process, Renewal theory, Markov chains, Reversibility, Queueing networks, Martingales, Random

walk.

Course outcomes
Students would be able to model complex systems with uncertainty using random processes, and analyze the

system performance.
Grading policy
20% Assignments

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20% Mid-term

20% Project

40% Final
Assignments

Resources

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