Advantages and a Limitation of Using LEG Nets in a Real-TIme Problem
This work addresses decision-making under uncertainty in diagnostic-like scenarios, but it is incremental as it focuses on documenting the use and a limitation of an existing method.
The paper applied a Bayesian method to a real-time problem resembling medical diagnosis, where decisions must be made with incomplete data, and identified a specific limitation of the method.
After experimenting with a number of non-probabilistic methods for dealing with uncertainty many researchers reaffirm a preference for probability methods [1] [2], although this remains controversial. The importance of being able to form decisions from incomplete data in diagnostic problems has highlighted probabilistic methods [5] which compute posterior probabilities from prior distributions in a way similar to Bayes Rule, and thus are called Bayesian methods. This paper documents the use of a Bayesian method in a real time problem which is similar to medical diagnosis in that there is a need to form decisions and take some action without complete knowledge of conditions in the problem domain. This particular method has a limitation which is discussed.