 # Probability Theory

Probability Theory
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.
MATH
431
 Hours 3.0 Credit, 3.0 Lecture, 0.0 Lab Prerequisites MATH 313; or MATH 213 Taught Fall odd years Programs Containing MATH 431
Course Outcomes:

### Reasoning

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.

### Probability

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.

### Variables

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.

### Statistical Inference

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.