IE502: Probabilistic Models

Prof. Jayendran Venkateswaran



There is randomness and unpredictability in any process or phenomenon, thus when modelling, one must have a more probabilistic approach, as opposed to a deterministic one. This course will introduce you to models and techniques to deal with randomness that underlie in many industrial and social systems. It includes discussions on probability theory, probability models, their properties and their applications. The course revolves around the topics of Conditional Probability, Markov Chains, Poisson/Stochastic Processes and Queuing theory. The applications of these concepts are wide-ranging and can be found even in day-to-day life.


Course Content

  • Probability Theory
  • Reliability Theory
  • Random Variables
  • Markov Chains
  • Conditional Probability & Expectations
  • Elementary Queuing Theory
  • Exponential distribution & related models
  • Other applications


Grading Structure

Test 1: 10-14%

Test 2: 16-18%

Surprise class tests: 0-8%

Mid Sem: 24-26%

Final Exam: 40-44%



The course covers a lot of interesting topics that find applications in a lot of real life situations pertaining to mechanical and electrical engineering. The course looks at the topics from an engineering perspective and does not go into rigorous mathematical and statistical analysis. This is the perfect course for anyone who is interested in the industrial applications of probability and does not want go deep into the math surrounding it.



  • Sheldon M. Ross (2006) Introduction to Probability Models, 9th ed, Academic Press.
  • W. L. Winston (2003) Introduction to Probability Models: Operations Research, Volume II, 4th ed., Duxbury Resource Center.
  • H. A. Taha (2002) Operations Research: An Introduction, 8th ed, Prentice Hall.
  • D. P. Bertsekas and J. N. Tsitsiklis (2008) Introduction to Probability, 2nd ed, Athena Scientific.