Using group theory for knowledge representation and discovery

In this paper, we present an approach to extract most relevant information from data given in form of a probability distribution. Relevance here is meant with respect to some appropriate inductive inference process, like maximum entropy inference (ME-inference) in probabilistics. So in particular, the method developed in this paper is apt to solve the inverse maxent problem, computing from a distribution in a non-heuristic way a set of conditionals that ME-represents that distribution. Since we only make use of one special characteristic of ME-inference, this method may as well be applied to other, similar inference processes.