E2 204, Spring 2020
Stochastic Processes & Queueuing Theory
Lectures
- 03 Jan 2020: Lecture-00 introduction
- 14 Jan 2020: Lecture-01 Probability Review
- 16 Jan 2020: Lecture-02 Conditional Expectation
- 17 Jan 2020: Lecture-03 Stochastic Processes
- 21 Jan 2020: Lecture-04 Stopping Times
- 23 Jan 2020: Lecture-05 Strong Markov Property
- 24 Jan 2020: Lecture-06 Renewal Processes
- 28 Jan 2020: Lecture-07 Distribution Functions
- 30 Jan 2020: Lecture-08 Limit Theorems
- 04 Feb 2020: Lecture-09 Regenerative Processes
- 06 Feb 2020: Lecture-10 Blackwell’s Theorem
- 07 Feb 2020: Lecture-11 Key Renewal Theorem
- 12 Feb 2020: Lecture-12 Key Renewal Theorem: Applications
- 14 Feb 2020: Lecture-13 Renewal Reward Process
- 18 Feb 2020: Lecture-14 Discrete Time Markov Chains
- 20 Feb 2020: Lecture-15 DTMC: Invariant Distribution
- 27 Feb 2020: Lecture-16 Continuous Time Markov Chains
- 03 Mar 2020: Lecture-17 CTMC: Embedded Markov Chain and Jump Times
- 05 Mar 2020: Lecture-18 CTMC: Stationarity
- 10 Mar 2020: Lecture-19 Reversibility
- 12 Mar 2020: Lecture-20 Queues
- 17 Mar 2020: Lecture-21 Reversed Processes
- 19 Mar 2020: Lecture-22 Stochastic Networks
- 24 Mar 2020: Lecture-23 Martingales: Introduction
- 26 Mar 2020: Lecture-24 Martingales: Convergence Theorem
- 31 Mar 2020: Lecture-25 Martingales: Concentration Inequalities
- 02 Apr 2020: Lecture-26 Exchangeability
- 07 Apr 2020: Lecture-27 Random Walks
- 09 Apr 2020: Lecture-28 GI/G/1 Queues
Homework
- 09 Jan 2020: Homework-01
- 23 Jan 2020: Homework-02
- 06 Feb 2020: Homework-03
- 13 Feb 2020: Homework-04
- 20 Feb 2020: Homework-05
- 05 Mar 2020: Homework-06
- 19 Mar 2020: Homework-07
- 03 Apr 2020: Homework-08
Tests
- 25 Jan 2020: Quiz 1
- 08 Feb 2020: Quiz 2
- 24 Feb 2020: Mid Term 1
- 29 Feb 2020: Quiz 3
- 07 Mar 2020: Quiz 4
- 14 Mar 2020: Mid Term 2
- 28 Mar 2020: Quiz 5
- 11 Apr 2020: Quiz 6
- 18 Apr 2020: Final
Grading Policy
Mid Terms: 40
Quiz : 20
Final : 40
Course Syllabus
Poisson process, Renewal theory, Markov chains, Reversibility, Queueing networks, Martingales, Random walk.
Course Description
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.
Slack Information
Slack
Students can signup for course slack using their iisc.ac.in email at https://courses-ece-iisc.slack.com/signup Slack signup.
Add yourself to the public channel #spqt-2020.
Instructor
Parimal Parag
Office: EC 2.17
Hours: By appointment.
Time and Location
Classroom: EC 1.07, Main ECE Building
Hours: Tue/Thu 11:00am-12:30pm.
Teaching Assistants
Ajay Kumar Badita
Email: ajaybadita@iisc.ac.in
Hours: By appointment.
Textbooks
Stochastic Processes, P. Parag and Vinod Sharma.
Stochastic Processes, Sheldon M. Ross, 2nd edition, 1996.
Introduction to Stochastic Processes, Erhan Cinlar, 2013.
Markov Chains: Gibbs Fields, Monte Carlo Simulation, and Queues, Pierre Bremaud, 1999.
Markov Chains, James R. Norris, 1998.
Reversibility and Stochastic Networks, Frank P. Kelly, 2011.
Probability: Theory and Examples, Rick Durett, 4th edition, 2010.
