Bayes-Nets are a suitable means for probabilistic inference. Such nets are very restricted concerning the communication language with the user, however. MinREnt-inference in a conditional environment is a powerful counterpart to this concept. Here conditional expressions of high complexity instead of mere potential tables in a directed acyclic graph, permit rich communication between system and user. This is true as well for knowledge acquisition as for query and response. For any such step of probabilistic reasoning, processed information is measurable in the information theoretical unit [bit]. The expert-system-shell SPIRIT is a professional tool for such inference and allows realworld (decision-)models with umpteen variables and hundreds of rules.
Keywords: probabilistic reasoning -- Bayes-Nets -- entropy -- MinREnt-Inference -- SPIRIT