Decision Analysis

Catalog description

A first course in decision analysis that extends the domain of decision-making problems from those considered in traditional statistical hypothesis testing scenarios: modeling decisions, where the emphasis is on 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.