This course is the second of a two-course sequence that emphasizes modeling and analysis of real-world systems. Continuing from the modeling process introduced in SE387, this course introduces the stochastic modeling process and many of the classical stochastic models used by systems engineers, operations researchers and management professionals to capture and describe quantitative effects of uncertainty on decision-making as part of the Systems Decision Process (SDP). Topics include stochastic life cycle cost modeling, conditional probability models, basic inference chains, Markov Chains, Poisson Processes, birth and death processes, counting processes, queuing systems, and simulation. This course prepares cadets for the modeling required in follow-on courses, including SE481, EM484, SE485 and SE402/403. Cadets will spend several lessons in a computer lab environment.
