STAT 676 - Advanced Probability

Units: 4
Advanced treatment of probability theory and its applications. May include: expectation, conditioning, generating/characteristic functions, modes of convergence, limit theorems, bivariate distributions, Monte Carlo simulation, Markov processes, combinatorial techniques, Markov Chain Monte Carlo, and Bayesian models.

Prerequisites: STAT 620.
Possible Instructional Methods: Entirely On-ground.
Grading: A-F grading only.
Student Learning Outcomes - Upon successful completion of this course students will be able to:

Demonstrate the use of probability models.
Use the Law of Large Numbers to show convergence to the expectation of a random variable.
Use the Central Limit Theorem to show convergence to the limiting distribution of a random variable.
Use appropriate software to simulate random numbers from probability models and demonstrate the long-run properties for stochastic probability models.