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, and convert the parameters of the causal influence model into one or more entries of a conditional probability table, using a creation function; wherein the child node Y and each of the parent nodes X i (i=1,. .. , n) is one of: a discrete Boolean node having states true and false; a discrete Ordinal node having a plurality of ordered states; and a discrete Categorical node having a plurality of unordered states; and wherein the influence of each of the parent nodes X i on the child node Y is one of a promoting influence and an inhibiting influence; wherein said each one of the parent nodes X i has a promoting influence on the child node Y when a true state or a higher level state of the parent node increases the probability of the child node being in the true state or the higher level state; wherein said each one of the parent nodes X i has an inhibiting influence on the child node Y when a false state or a lower level state of the parent node increases the probability of the child node being in the true state or the higher level state; wherein the child node Y has a number m of states y k (k=1,. .. , m); and wherein each parent node X i (i=1,. .. , n) has a number m i of states x i j (j=1,. .. , m i ); wherein the n parent nodes X i (i=1,. .. , n) has a number l max of possible configurations X l (l=1,. .. , l max ), where l max = ∏ i = 1 n ⁢ m i ; and wherein x l represents the l-th configuration of the parent nodes X i (i=1,. .. , n) in which each parent node X i (i=1,. .. , n) is in a state x i j (j=1,. .. , m i ); wherein the probability distribution for all possible states of the child node Y comprises a set of values P(y k |x l ), where P(y k |x l ) represents the probability that the child node Y is in the state y k , given that the n parents of Y are in the configuration x l ; wherein the parameters of the causal influence model comprise at least one of: (i) 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; and (ii) 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 i ); 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 , and wherein the creation function includes a scale factor that keeps all probabilities P(y k |x l ) between 0 and one; and wherein the parent influence function V lk is a function of the causal influences h ijk ; and wherein V lk is represented mathematically by: 
 V lk =f ( h 1 jk ,h 2 jk ,. .. , h njk ), where parent node X i takes on a state x i j in the configuration x l , where ∑ k = 1 m ⁢ V lk = 0 , and where the value of the parent influence function V lk is between −1 and 1.