1. A computer implemented method for providing conditional probability data in a Bayesian Belief network based decision support apparatus, wherein the conditional probability data defines conditional probabilities of states values of a particular network node as a function of a vector of state values for a combination of parent nodes of the particular network node in the Bayesian Belief network, wherein at least one of the parent nodes has at least three possible state values, the computer implemented method comprising receiving elicited conditional probability data defining the conditional probabilities of the state values of the particular network node for a subset of all possible vectors of combinations of state values of the parent nodes; interpolating conditional probability data defining the conditional probabilities of the state values of the particular network node for further possible vectors of state values of the parent nodes from the elicited conditional probability data, using an interpolation function that interpolates between the elicited conditional probability data as a function of influence factors assigned to the vectors; receiving elicited data defining a ranking of state values of the at least one of the parent nodes, according to increasingly or decreasingly positive and/or negative influence of the state values of the at least one of the parent nodes on the state values of the particular node; determining the influence factors for the possible vectors of state values for the combination of the parent nodes of the particular node as a function of the ranking of the state values of the at least one of the parent nodes in the possible vectors.