In this paper, we address the problem of data description using a Bayesian framework. The goal of data descrip- tion is to draw a boundary around objects of a certain class of interest to discriminate that class from the rest of the feature space. Data description is also known as one-class learning and has a wide range of applications. The proposed approach uses a Bayesian framework to precisely compute the class boundary and therefore can utilize domain information in form of prior knowl- edge in the framework. It can also operate in the ker- nel space and therefore recognize arbitrary boundary shapes. Moreover, the proposed method can utilize un- labeled data in order to improve accuracy of discrimi- nation. We evaluate our method using various real-world datasets and compare it with other state of the art ap- proaches of data description. Experiments show promis- ing results and improved performance over other data description and one-class learning algorithms.