MATH
636
Advanced Probability 1
Hours
3.0 Credit, 3 Lecture, 0 Lab
Prerequisites
Measure-theoretic probability. Axioms for and construction of probability spaces. Random variables, expectation, uniform integrability, independence, convergence of sequences of random variables, conditioning.
Learning Outcomes
Students should understand the topics listed in the minimal learning outcomes on the Math 543 Wiki page. As evidence of that understanding, students should be able to demonstrate mastery of all relevant vocabulary, familiarity with common examples and counterexamples, knowledge of the content of the major theorems, understanding of the ideas in their proofs, and ability to make direct application of those results to related problems.
Overview
Probability spaces
Random variables
Independence
Expectation
Conditioning
Probability measures on product spaces
Generating functions
Discrete Markov chains