Joint work with Ida Moltke, Martin Thiim and Thomas Hamelryck (The Bioinformatics Center, University of Copenhagen) So far, the most common approach to modeling local RNA 3-D structure has been to describe the local conformational space as discrete in a non-probabilistic framework. We present an original approach to modeling local RNA 3-D structure, namely a probabilistic model that treats the conformational space as continuous. In our model the backbone dihedral angles and the base dihedral angles are modeled with a Dynamic Bayesian Network using directional statistics. The model assigns a probability distribution to the conformational space and therefore it has numerous applications. It allows for fast probabilistic sampling of locally RNA-like structures and it can therefore be used in RNA 3-D structure prediction, where one of the problems is how to efficiently search through the space of plausible RNA structures. Today, the state-of-the-art method for suggesting plausible RNA structures is based on assembling fragments from libraries. Further, the model can also be used for deriving probabilities of seeing different local structures and it can therefore be used for quality validation of experimentally determined structures.