This course is the second of a two-course sequence that emphasizes modeling and analysis of real-world systems. Building on the systems modeling approaches introduced in SE387, this course introduces uncertainty into design, modeling, parameter estimations, and data as they effect many of the classical stochastic models used by systems engineers, operations researchers and management professionals to capture and describe quantitative effects of uncertainty on systems design and analysis, and on decision-making as part of the Systems Decision Process (SDP). Topics include stochastic value modeling, flaw of averages, reliability, realization analysis, Bayesian updating, conditional probability models, and simulation. This course prepares cadets for the quantitative reasoning and analysis techniques required in follow-on courses, including SE481, EM484, SE485 and SE402/403. Cadets will spend several lessons in a computer lab environment.
