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.
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.
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.
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.
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 Design 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.
This course is an integral part of the Systems Engineering major and emphasizes the understanding of concepts underlying many of the models used by systems engineers to capture and describe quantitative effects of uncertainty on decision-making. Continuing from the modeling process introduced in previous courses, SE388 introduces cadets to stochastic models that describe how system behavior evolves/changes over time in the presence of uncertainty or probabilistic effects. It emphasizes models of random events that occur in real world examples. The topics covered include conditional probability models, basic inference chains, Markov Chains, Poisson Processes, birth and death processes, counting processes, queuing systems, and simulation. Applications include flow of evidence,telecommunication systems, combat systems, and industrial production and distribution systems. This course also helps to enhance the cadetsý understanding of probabilistic aspects of systems by augmenting the content taught in SE375 and MA206. This course prepares SE/OR majors for the modeling required in follow-on courses, including SE481, SE485 and SE402/403. Cadets will spend two to six lessons in a computer lab environment.
This course is an integral part of the Systems Engineering major and emphasizes the understanding of concepts underlying many of the models used by systems engineers. Continuing from the modeling process introduced in previous courses, SE388 introduces cadets to stochastic models that describe how system behavior evolves/changes over time. It emphasizes models of random events that occur in real world examples. The topics covered include Markov Chains, Poisson Processes, birth and death processes, counting processes, queuing systems, and simulation. Applications include telecommunication systems, combat systems, and industrial production and distribution systems. This course also helps to enhance the cadetsý understanding of probabilistic aspects of systems by augmenting the content taught in SE375 and MA206. This course prepares SE/OR majors for the modeling required in follow-on courses, including SE481, SE485 and SE402/403. Cadets will spend two to six lessons in a computer lab environment.
Emphasizes the understanding of concepts underlying many of the models used by Systems Engineers. It introduces cadets to stochastic models that describe how systems change over time. It emphasizes models of random events that occur in real world examples. The topics covered include Markov Chains, Poisson Processes, queue processes, and reliability processes. Applications are from many areas, including telecommunication systems, combat systems, and industrial production and distribution systems. Communication skills are developed with both written reports and oral presentations. Cadets will spend two to six lessons in a computer lab environment.
The second of a two-course sequence that is part of the engineering science foundation for subsequent courses in systems engineering. This course introduces cadets to many of the classical stochastic models used by systems engineers and continues to emphasize the modeling process studied in SE387. Topics include inventory models, forecasting techniques, queuing systems, discrete event simulations, and a brief introduction to decision analysis. The cadet will be able to apply skills learned in this course to many military planning and resource allocation problems. The stochastic models extend and build on the deterministic models studied in SE387. Cadets are introduced to discrete event simulation as an analysis alternative to analytical models. A major course exercise involves cadets building a spreadsheet simulation model. Communication skills are developed with both written reports and oral discussions.
Lessons
40 @ 55 min (2.500 Att/wk)
Labs
0 @ 0 min
Special requirements
Two group design exercises; compensatory time provided.
