SIMULATION AND PROBABILISTIC MODELING

Prerequisites & course information

Requisites: ( MATH 331 , STAT/MATH 309 , 409, 431 , 531, STAT 311 , or concurrent enrollment) and ( I SY E 210 , E C E 331 , MATH/STAT 310 , 410, STAT 312 , 324 , 371 , or concurrent enrollment) and ( MATH 320 , 340 , 345 , or concurrent enrollment), or Engr Guest Student Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Apply knowledge of math, science, and engineering principles to model real stochastic systems Audience: Undergraduate 2. Identify, formulate, and solve problems using appropriate data analytic and simulation approaches Audience: Undergraduate 3. Understand and apply probabilistic modeling techniques such as Markov Chains and queueing theory to study stochastic systems Audience: Undergraduate 4. Apply statistical methods to analyze stochastic behaviors of the systems Audience: Undergraduate 5. Apply simulation software to model the process and evaluate performance measures of the systems Audience: Undergraduate Learning Outcomes: 1. Apply knowledge of math, science, and engineering principles to model real stochastic systems Audience: Undergraduate 2. Identify, formulate, and solve problems using appropriate data analytic and simulation approaches Audience: Undergraduate 3. Understand and apply probabilistic modeling techniques such as Markov Chains and queueing theory to study stochastic systems Audience: Undergraduate 4. Apply statistical methods to analyze stochastic behaviors of the systems Audience: Undergraduate 5. Apply simulation software to model the process and evaluate performance measures of the systems Audience: Undergraduate