We present a novel discriminative-generative hybrid approach in this paper, with emphasis on application in multiview object detection. Our method includes a novel generative model called Random Attributed Relational Graph (RARG) which is able to capture the structural and appearance characteristics of parts extracted from objects. We develop new variational learning methods to compute the approximation of the detection likelihood ratio function. The variaitonal likelihood ratio function can be shown to be a linear combination of the individual generative classifiers defined at nodes and edges of the RARG. Such insight inspires us to replace the generative classifiers at nodes and edges with discriminative classifiers, such as support vector machines, to further improve the detection performance. Our experiments have shown the robustness of the hybrid approach - the combined detection method incorporating the SVM-based discriminative classifiers yields superior detection performances compared to prior works in multiview object detection.