Approximate Bayesian Networks
Dominik Ślȩzak · Studies in fuzziness and soft computing · 2002
We introduce the notion of an approximate Bayesian network, which almost keeps the information entropy of data and encodes knowledge about approximate dependencies between features. Presented theoretical results, as well as relationships to fundamental concepts of the rough set theory, provide a novel methodology of applying the Bayesian net models to the real life data analysis. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.