1. An apparatus for making a probabilistic inference based on a belief network including a child node Y and one or more parent nodes X i (i=1,. .. , n) for the child node Y, the apparatus comprising: a processing system configured to receive as input one or more parameters of a causal influence model that describes influence of the parent nodes X i on possible states of the child node Y, the processing system further configured to convert the parameters of the causal influence model into one or more entries of a conditional probability table, using a creation function; wherein the conditional probability table provides a probability distribution for all the possible states of the child node Y, for each combination of possible states of the parent nodes X i ; wherein the parameters of the causal influence model comprise at least one of: a baseline probability b k (k=1,. .. , m) for each state y k (k=1,. .. m) of the child node Y, wherein the baseline probability b k represents a probability that the child node Y is in the state y k when none of the parent nodes X i (i=1,. .. , n) exert any influence on the child node Y; a causal influence h ijk (i=1. .. , n; j=1. .. m i ; k=1. .. m), wherein the causal influence h ijk represents a change in the probability of the state y k (k=1,. .. , m) of the child node Y, when the i-th parent node X i (i=1,. .. , n) is in a state x i j (j=1,. .. , m); and wherein the creation function includes a parent influence function V lk , and wherein the parent influence function V lk represents an overall influence of the parent nodes X i (i=1,. .. , n) on a probability P(y k |x l ) that the child node Y is in the state y k given that the parent nodes are in the configuration x l .