Model uncertainty: theory and practical implications

Abstract
A model is a representation of a system that can be used to answer questions about the system. In many situations in which models are used, there exists no set of univerally accepted modeling assumptions. The term model uncertainty commonly refers to uncertainty about a model's structure, as distinguished from uncertainty about parameters. This paper presents alternative formal approaches to treating model uncertainty, discusses methods for using data to reduce model uncertainty, presents approaches for diagnosing inadequate models, and discusses appropriate use of models that are subject to model uncertainty.

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