This course provides a rigorous, measure-theoretic introduction to probability theory. It begins with the notion of fields, sigma-fields, and measurable spaces, and also surveys elements from integration theory and introduces random variables as measurable functions. It then examines the axioms of probability theory and fundamental concepts including conditioning, conditional probability and expectation, independence, and modes of convergence. Other topics covered include characteristic functions, basic limit theorems (including the weak and strong laws of large numbers), and the central limit theorem.
Course prerequisites: 
625.401 Real Analysis and 625.403 Statistical Methods and Data Analysis.
Course instructor: 
Hill

View Course Homepage(s) for this course.

Course all programs: 
Applied and Computational Mathematics
Data Science