Statistics

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. (Formerly AMS 261.)

Requirements

Prerequisite(s): STAT 205B or by permission of instructor. Enrollment is restricted to graduatestudents.

Credits

5

Instructor

Athanasios Kottas