A GENERAL ALGORITHM FOR DETERMINING LIKELIHOOD RATIOS IN CASCADED INFERENCE

• Main Author: MARTIN, ANNE WILLS
• Format: Others
• Language: English
• Published: 2007
• Subjects: Operations Research
• Online Access: http://hdl.handle.net/1911/15631

In cascaded inference tasks there is not a direct logical connection between an observable event (datum) and the hypothesis of interest. Instead there is interposed at least one logical reasoning stage, consisting of intervening variables or intermediate event states. This paper is concerned with the modification or extension of Bayes' rule to render it more specific as a normative model for cascaded inference. In particular, the work reported here is directed towards simplifying the task of the researcher who wishes to use Bayes' rule as a standard for inferential behavior and of the analyst who wishes to use task decomposition in aiding inference. This is achieved by the development of some general principles of inference, the use of concepts from graph theory for the representation of inference tasks, and the application of computer technology.