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In this paper, we consider a methodology that utilizes qualitative expert knowledge for inference in a Bayesian network. The decision-making assumptions and the mathematical equation for Bayesian inference are derived based on data and knowledge obtained from experts. A detailed method to transform knowledge into a set of qualitative statements and an “a priori” distribution for Bayesian probabilistic models are proposed. We also propose a simplified method for constructing the “a prior” model distribution. Each statement obtained from the experts is used to constrain the model space to the subspace which is consistent with the statement provided. Finally, we present qualitative knowledge models and then show a full formalism of how to translate a set of qualitative statements into probability inequality constraints.