MTH 550 Probability and Stochastic Processes
Instructor: Lubos Thoma
Office: Tyler Hall 214
Class schedule: MW 4.30 - 5.45pm, Ballentine 102
Office hours: MW 3.00--4.00pm, F 1.00--2.00pm
This is a graduate class in probability theory and random processes
for students in mathematics, engineering, finance, and computer science.
Prerequisites are MTH 451 (Probability) or an equivalent course,
linear algebra, and some advanced calculus.
The purpose of the course is to present the basic concepts and
techniques of probability theory as well as some of their applications.
Emphasis will be placed on fundamental principles, thinking
probabilistically, and methods and results of modern probability theory.
Topics will include:
basic properties of probability measures,
discrete and continuous random variables, distributions, random walks,
generating functions, limit theorems, large deviations,
Markov chains and Markov processes, branching processes, Poisson processes,
martingales, Brownian motion.
To illustrate the general theory
the class will include many applications
(taking into account interests of the audience)
to mathematics (e.g. discrete mathematics, percolations),
engineering (e.g. signal processing),
computer science (e.g. analysis of random(ized) algorithms),
and mathematical finance.
G. Grimmett and D. Stirzaker,
Probability and Random Processes,
Oxford University Press, third edition,
N. Alon, J. Spencer, The Probabilistic Method,
I. Sinai, Probability Theory: An Introductory Course,