MATH 355

Introduction to Stochastic Processes

2 Undergraduate credits
Effective May 7, 2019 – Present

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.

Prerequisites

Linear Algebra and Applications and Probability and Introduction to Mathematical Modeling

Special information

Note: Students whose prerequisites are not identified by the system would contact the Math and Statistics Department for an override at MATH@metrostate.edu. First day attendance required except by instructor permission.

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.

Spring 2020

Section	Title	Instructor
01	Introduction to Stochastic Processes	Jacobson, Da	Books	Course details