1. A method, comprising executing computer executable instructions by one or more processors for automatically determining a multinomial distribution in a Bayesian network, the method further comprising: constructing a graphical structure of a Bayesian network using a set of joint probability parameters associated to the constructed Bayesian network structure; designing an automatic procedure to determine values associated with said joint probability parameters; simultaneously deriving an equivalent mean Bayesian network or a class of Bayesian networks; and performing quantitative predictions and reasoning simulations using the equivalent mean Bayesian network or the class of Bayesian networks wherein a matrix is constructed which places a relative order over the child's probability under a combination of activating and inhibiting parent notes, wherein matrix values P i,j are calculated as P 0 , 0 = P ⁡ ( D , A _ ) P ⁡ ( D , A _ ) + P ⁡ ( D _ , A _ ) , and ⁢ ⁢ P 0 , 1 = P ⁡ ( D , A ) P ⁡ ( D , A ) + P ⁡ ( D _ , A ) wherein P i,j , indicates child's probability in the presence of j activating parent nodes and i inhibiting parent nodes; P 0,0 indicates child's probability in the presence of 0 activating parent nodes and 0 inhibiting parent nodes, and P 0,1 indicates child's probability in the presence of 1 activating parent nodes and 0 inhibiting parent nodes; and wherein value P i,j is represented by the joint probability.