In this paper we present a probabilistic framework for case-based reasoning in data-intensive domains, where only weak prior knowledge is available. In such a probabilistic viewpoint the attributes are interpreted as random variables, and the case base is used to approximate the underlying joint probability distribution of the attributes. Consequently structural case adaptation (and parameter adjustment in particular) can be viewed as prediction based on the full probability model constructed from the case history. The methodology addresses several problems encountered in building case-based reasoning systems. It provides a computationally efficient structural adaptation algorithm, avoids over-fitting by using Bayesian model selection and uses directly probabilities as measures of similarity. The methodology described has been implemented in the D-SIDE software package, and the approach is validated by presenting empirical results of the method's classification prediction performance for a set of public domain data sets.