STAT246: Probability Theory with Markov Chains

Introduction to probability theory: probability spaces, expectation as Lebesgue integral, characteristic functions, modes of convergence, conditional probability and expectation, discrete-state Markov chains, stationary distributions, limit theorems, ergodic theorem, continuous-state Markov chains, applications to Markov chain Monte Carlo methods. Prerequisite(s): STAT 203. Enrollment is restricted to graduate students.

5 credits

Year Fall Winter Spring Summer

Formerly AMS 0261

While the information on this web site is usually the most up to date, in the event of a discrepancy please contact your adviser to confirm which information is correct.