Bayesian Inference by Combining Training Data and Background Knowledge Expressed as Likelihood Constraints
2009 (English)In: International Journal of Approximate Reasoning, ISSN 0888-613XArticle in journal (Other academic) Submitted
Bayesian inference, or classification, from data is a powerful method for determining states of process when no detailed physical model of the process exists. However, the performance of Bayesian inference from data is dependent on the amount of training data available. In many real applications the amount of training data is limited, and inference results become insufficient. Thus it is important to take other kinds of information into account in the inference as well. In this paper, we consider a general type of background knowledge that appears in many real applications, for example medical diagnosis, technical diagnosis, and econometrics. We show how it can be expressed as constraints on the likelihoods, and provide detailed description of the computations. The method is applied to a diagnosis example, where it is clearly shown how the integration of background knowledge improves diagnosis when training data is limited.
Place, publisher, year, edition, pages
Engineering and Technology
IdentifiersURN: urn:nbn:se:liu:diva-51926OAI: oai:DiVA.org:liu-51926DiVA: diva2:278130