Statistics: Introduction to Stochastic Processes I

Description

Modeling how time-dependent random phenomena can evolve over time is a valuable tool used to analyze processes across a wide range of industries. This course focuses on building a framework to formulate and analyze probabilistic systems to understand potential outcomes and inform decision-making.

Prerequisites

MS&E220 or equivalent with consent of instructor.

Topics include

  • Continuous-time Markov chain
  • Discrete-time Markov chain
  • Queuing theory
  • Renewal processes

Course Availability

The course schedule is displayed for planning purposes – courses can be modified, changed, or cancelled. Course availability will be considered finalized on the first day of open enrollment. For quarterly enrollment dates, please refer to our graduate education section.