Probability and Random Processes

Catalog description

Framework for probability theory: probability spaces as measure spaces, random variables, expectation and conditional probability. Major results such as limit theorems for sums of random variables, zero-one laws, and ergodic theorems. Applications may include branching processes, Markov Chains, Markov Random Fields, martingales, percolation, Poisson Processes, queuing theory, random walks, and renewal processes. Prerequisite(s): for undergraduates and enrollment requirement for graduate students: MATH 430 with a grade of B (3.0) or better, OR ( MATH 430 and either MATH 440 or MATH 441 and MATH 470) with a grades of C (2.0) or better.