Probabilistic and Bayesian Representations of Uncertainty in Information Systems: A Pragmatic Introduction
A great deal has been written about the underlying principles of alternative methods of representing uncertainty—not the least about probability and Bayesian methods. While we cannot entirely resist discussing basic principles, we will focus on the pragmatic issues, which too often get lost under the mass of philosophy and mathematics. We will address such questions as: How can we use probability to represent the various types of uncertainty? How can we quantify these uncertainties? How much effort is necessary to do so? How can we obtain the greatest benefits from representing uncertainty while minimizing the effort? There are a variety of reasons to represent uncertainty and a variety of probabilistic and Bayesian ways to do so, requiring varying amounts of effort. We discuss an approach to resolve these issues, so that the costs will be commensurate with the benefits.