## E2 204 : Stochastic Processes & Queueing Theory, Spring 2018## Lectures & Homework02 Jan 2018: Lecture-01 Introduction 04 Jan 2018: Lecture-02 Probability Review 09 Jan 2018: Lecture-03 Probability Review 11 Jan 2018: Lecture-04 Introduction to Renewal Theory 16 Jan 2018: Lecture-05 Concentration of Renewal Processes 18 Jan 2018: Lecture-06 Regenerative Processes 23 Jan 2018: Lecture-07 Renewal Equation and Blackwell Theorem 25 Jan 2018: Lecture-08 Key Renewal Theorem 30 Jan 2018: Lecture-09 Applications: Age-dependent Branching and Delayed Renewal 01 Feb 2018: Lecture-10 Applications: Equilibrium Renewal and Renewal Reward 06 Feb 2018: Lecture-11 Markov Chains 08 Feb 2018: Lecture-12 Markov Chains: Equilibrium Distribution 13 Feb 2018: Lecture-13 Continuous Time Markov Chains 15 Feb 2018: Lecture-14 Embedded Markov Chain and Holding Times 22 Feb 2018: Lecture-15 Markov Processes: Stationarity 27 Feb 2018: Lecture-16 Markov Processes: Uniformization 01 Mar 2018: Lecture-17 Reversibility 06 Mar 2018: Lecture-18 Reversed Processes 08 Mar 2018: Lecture-19 Stochastic Networks 13 Mar 2018: Lecture-20 Martingales 15 Mar 2018: Lecture-21 Martingale Convergence Theorem 20 Mar 2018: Lecture-22 Martingale Concentration Inequalities 22 Mar 2018: Lecture-23 Exchangeability 27 Mar 2018: Lecture-24 Random Walks 29 Mar 2018: Lecture-25 Random Walks: GI/G/1 Queue 03 Apr 2018: Lecture-26 Reversible Markov Chains 05 Apr 2018: Lecture-27 Reversible Markov Chains 10 Apr 2018: Lecture-28 Reversible Markov Chains
## Homeworks24 Jan 2018: Homework-01 Due Friday, Jan 25 Solutions. 30 Jan 2018: Homework-02 Due Friday, Feb 09 Solutions. 15 Feb 2018: Homework-03 Due Friday, Feb 23 Solutions. 03 Mar 2018: Homework-04 Due Friday, Mar 09 19 Mar 2018: Homework-05 Due Friday, Mar 23 25 Mar 2018: Homework-06 Due Friday, Apr 06 31 Mar 2018: Homework-07 Due Friday, Apr 13 Solutions
## Tests## Grading PolicyMid Term: 20 ## Course SyllabusPoisson process, Renewal theory, Markov chains, Reversibility, Queueing networks, Martingales, Random walk. ## Course DescriptionBasic 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 InformationStudents can signup for course slack using their iisc.ac.in email at Slack signup. Add yourself to #spqt-2018. ## InstructorsParimal Parag ## Time and LocationClassroom: EC 1.07, Main ECE Building ## Teaching AssistantRahul Ramachandran ## TextbooksStochastic 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. |