MATH 355 Introduction to Stochastic Processes
Stochastic processes involve sequences of events governed by probabilistic laws. Many applications of stochastic processes occur in biology, medicine, psychology, finance, telecommunications, insurance, security, and other disciplines. This course introduces the basics of applied stochastic processes such as Markov chains (both discrete-time and continuous-time), queuing models, and renewal processes. Software is used to solve real-world problems with an emphasis on interpretation of results and the role of stochastic processes in management decision-making.
Note: Students whose prerequisites are not identified by the system should contact the Math and Statistics Department for an override at MATH@metrostate.edu.
Prerequisites
Special information
2 Undergraduate credits
Effective May 7, 2019 to present
Learning outcomes
General
- Identify and apply the most appropriate stochastic process technique for a given applied problem.
- Apply probability and matrix theory to solve stochastic models.
- Solve stochastic process problems mathematically and using software.
- Asses how sensitive stochastic models are to changes that might occur in model variables.
- Interpret and understand the solution for a stochastic process application.
- Document and articulate the results and conclusions for stochastic process techniques applied to actual cases in a variety of disciplines.
Fall 2021
| Section | Title | Instructor | ||
|---|---|---|---|---|
| 21 | Introduction to Stochastic Processes | Jacobson, David Willia | Books | Course details |
| 50 | Introduction to Stochastic Processes | Jacobson, David Willia | Books | Course details |