MATH 536
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Applied Discrete Probability
Mathematics
College of Computational, Mathematical, & Physical Sciences
Course Description
Rigorous probability without the use of measure theory. Intensive study of finite and countable probability spaces. Applications to particle statistics, random walks, Markov decision processes, information theory, and asset pricing.
When Taught
Winter Even Yrs..
Min
3
Fixed
3
Fixed
3
Fixed
0
Other Prerequisites
Math 341
Title
Prove
Learning Outcome
Students will be able to prove major theorems from the theory of probability on countable spaces, and be able to perform similar derivations.
Title
Calculate
Learning Outcome
Students will be able to calculate probabilistic quantities in the context of countable probability spaces.
Title
Explain
Learning Outcome
Students will be able to provide intuitive explanations of major probability concepts.
Title
Model
Learning Outcome
Students will be able to appropriately model various phenomenon probabilistically.
