MATH 431
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Probability Theory
Mathematics
College of Computational, Mathematical, & Physical Sciences
Course Description
Axiomatic probability theory, conditional probability, discrete / continuous random variables, expectation, conditional expectation, moments, functions of random variables, multivariate distributions, laws of large numbers, central limit theorem.
When Taught
Fall Odd Years
Min
3
Fixed
3
Fixed
3
Fixed
0
Title
Probability
Learning Outcome
The ability to use and simulate random variables, distribution functions, probability mass functions, and probability density functions, through calculus and functional transformations, to answer quantitative questions about the outcomes of probabilistic systems.
Title
Variables
Learning Outcome
The ability to use and simulate multivariate distributions, independence, conditioning, and functions of random variables, including the ability to compute expectations, moments, and correlation functions, to describe relationships between different experimental conditions.
Title
Reasoning
Learning Outcome
The ability to use probabilistic reasoning and the foundations of probability theory to describe probabilistic engineering experiments in terms of sample spaces, event algebras, classical probability, and Kolmogorov's axioms.
Title
Statistical Inference
Learning Outcome
The ability to use statistics from measurements and acquired data to make reasonable quantitative inferences about engineering systems.
For more detailed information visit the Math 431 Wiki page.
