MA 261: Probability Models

Course Syllabus

Sample spaces, events, probability, discrete and continuous random variables, Conditioning and independence, Bayes formula, moments and moment generating function, characteristic function, laws of large numbers, central limit theorem, Markov chains, Poisson processes.

Assignments

• Markov Chains, due by Nov 12, 2018.
• Conditional Probability, due by Sep 17, 2018.
• Discrete Probability spaces, due by Sep 3, 2018.

Grading

There will be one midterm and a final examination. Please note the rules for the examinations. The weightages in the final grades are as follows:
• Assignments: 10%.
• Midterm: 40%.
• Final: 50%.

Suggested books

1. Ross, S.M. , Introduction to Probability Models ,Academic Press 1993.
2. Taylor, H.M., and Karlin, S., An Introduction to Stochastic Modelling ,Academic Press, 1994.
3. Feller, W. An Introduction to Probability Theory and Its Applications, Volume 1, Wiley 1968.

Course Details

• Instructor: Siddhartha Gadgil
• E-mail: siddhartha.gadgil@gmail.com.
• Office: N-15, Department of Mathematics.
• Lecture Timings: Monday, Wednesday, Friday : 9:00 am - 10:00 am.
• Lecture Venue: LH-1 (if available) or LH-5, Department of Mathematics.
• Teaching Assistants:
  • Srikanth Srimala, e-mail: srikanths@iisc.ac.in
  • Somnath Pradhan, e-mail: somnathp@iisc.ac.in
• Tutorial Timings: Friday : 5:30 pm - 6:30 pm.
• Tutorial Venue: LH-4, Department of Mathematics.