MATH 636
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Advanced Probability 1
Mathematics
College of Computational, Mathematical, & Physical Sciences
Course Description
Measure-theoretic probability. Axioms for and construction of probability spaces. Random variables, expectation, uniform integrability, independence, convergence of sequences of random variables, conditioning.
When Taught
Fall Even Years
Min
3
Fixed/Max
3
Fixed
3
Fixed
0
Title
Overview
Learning Outcome
Probability spaces
Random variables
Independence
Expectation
Conditioning
Probability measures on product spaces
Generating functions
Discrete Markov chains
Title
Learning Outcomes
Learning Outcome
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.
