Uncertainty and Imprecision: Modelling and Analysis

Abstract

Any statistical analysis or decision analysis contains numerical inputs of which we are unsure. Some of our uncertainty arises from physical randomness which we can model in various ways, ideally through probability. Some uncertainty relates to judgemental estimates of quantities about which we may be unsure in many different respects. There are other uncertainties involved, however: some relate to ambiguity and imprecision of meaning; some relate to lack of clarity in the objectives which the analysis seeks to meet; some relate to the numerical accuracy of calculations. How should the uncertainty arising from ambiguity be modelled? Other uncertainties can also impact on an analysis. Why is the analysis being conducted? Are the objectives clear?

Keywords:

• Bayesian Methods
• Decision Analysis
• Descriptive and Normative Models
• Fuzzy Sets and Possibility Theory
• Modelling Imprecision
• Prescriptive Analysis
• Probability Modelling
• Requisite Modelling
• Sensitivity Analysis