The majority of Bayesian networks learning and inference algorithms rely on the assumption that all random variables are discrete, which is not necessarily the case in real-world problems. In situations where some variables are continuous, a trade-off between the expressive power of the model and the computational complexity of inference has to be done: on one hand, conditional Gaussian models are computationally efficient but they lack expressive power; on the other hand, mixtures of exponentials (MTE), bases or polynomials are expressive but this comes at the expense of tractability. In this paper, we propose an alternative model that lies in between. It is composed of a "discrete" Bayesian network (BN) combined with a set of monodimensional conditional truncated densities modeling the uncertainty over the continuous random variables given their discrete counterpart resulting from a discretization process. We show that inference computation times in this new model are close to those in discrete BNs. Experiments confirm the tractability of the model and highlight its expressive power by comparing it with MTE.