University at Buffalo 3 credits Industrial and Systems Engineering
Decision analysis that extends the domain of decision-making problems from those considered in traditional statistical hypothesis testing scenarios: modeling decisions, which emphasize structuring decision problems using techniques such as influence diagrams and decision trees; modeling uncertainty, which covers subjective probability assessment, use of classical probability models, Bayesian analysis, and value of information; and modeling preferences, which introduces concepts of risk preference, expected utility, and multi-attribute value and utility models. This course is dual-listed with IE 512. Pre-Requisite: EAS 305 or CIE 308 or EE 305 or CE 305 or MTH 411 or STA 301.
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