Stochastic Processes

Welcome to the “Stochastic Processes” (CE-40695) course! This is a graduate level course that aims to provide a fundamental understanding of stochastic processes for computer science students.

Main References of the Course

  • Athanasios Papoulis and S. Unnikrishna Pillai, “Probability, Random Variables and Stochastic Processes,” McGraw-Hill Europe, 4th edition, Jan., 2002.
  • Robert G. Gallager, “Stochastic Processes: Theory for Applications,” Cambridge University Press, 1st edition, Feb., 2014.
  • George Casella and Roger L. Berger, “Statistical Inference,” Wadsworth Press, 2nd edition, Jun., 2001.

Homework (in Persian)

Handwritten Notes (in Persian)

These are the notes that I wrote in fall 2015 when I taught this course for the first time. Before use the notes, first please read this post (in Persian) in my Blog about them.

  • Note 1: A short review to probability
  • Note 2: Fundamentals and general concepts of stochastic processes
  • Note 3: Poisson process
  • Note 4: Gaussian process
  • Note 5: Markov chains
  • Note 6: Estimation theory

Sample Simulation Codes

Having access to computers and programming languages that can produce (though approximately) many random variables, it is a great opportunity that many aspects of stochastic processes can be show by writing simulation programs. In this page, I will gradually add more simulations that help students to have a better understanding of many stochastic processes concepts.