Probability is the mathematics of uncertainty.  We begin by developing the basic tools of probability theory, including counting techniques, conditional probability, and Bayes's Theorem.  We then survey several of the most common discrete and continuous probability distributions (binomial, Poisson, uniform, normal, and exponential, among others) and discuss mathematical modeling using these distributions. Often we cannot calculate probabilities exactly, and we need to approximate them.  A powerful tool here is the Central Limit Theorem, which provides the link between probability and statistics.  Another strategy when exact results are unavailable is simulation.  We examine Markov chain Monte Carlo methods, which offer a means of simulating from complicated distributions.  

Units: 1

Max Enrollment: 24

Crosslisted Courses:

Prerequisites: MATH 205

Instructor: Tannenhauser

Distribution Requirements: MM - Mathematical Modeling and Problem Solving

Semesters Offered this Academic Year: Fall

Notes: