STOCHASTIC MODELING TECHNIQUES

Prerequisites & course information

Requisites: ( STAT 311 , STAT/MATH 309 , 409, or 431 ) and ( MATH 320 , 340 , 341 , 345 , or 375 ), graduate/professional standing, or member of Engineering Guest Students Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Spring 2026 Learning Outcomes: 1. Identify and interpret basic concepts of probability, including random variables, and conditional probability, expectation, and variance Audience: Both Grad & Undergrad 2. Analyze probability models such as the Markov chains, the exponential distribution, Poisson processes, and queuing models Audience: Both Grad & Undergrad 3. Identify, formulate, and develop solution techniques for problems that can be modeled by stochastic models in various domains (engineering, computer science, supply chains, healthcare systems, operations research) Audience: Both Grad & Undergrad 4. Recognize how and when to apply each type of probability model Audience: Both Grad & Undergrad 5. Formulate mathematical formulations and apply proof techniques Audience: Graduate Learning Outcomes: 1. Identify and interpret basic concepts of probability, including random variables, and conditional probability, expectation, and variance Audience: Both Grad & Undergrad 2. Analyze probability models such as the Markov chains, the exponential distribution, Poisson processes, and queuing models Audience: Both Grad & Undergrad 3. Identify, formulate, and develop solution techniques for problems that can be modeled by stochastic models in various domains (engineering, computer science, supply chains, healthcare systems, operations research) Audience: Both Grad & Undergrad 4. Recognize how and when to apply each type of probability model Audience: Both Grad & Undergrad 5. Formulate mathematical formulations and apply proof techniques Audience: Graduate