User:Andre.eds/Darwinian networks2

Darwinian networks (DNs) were proposed to simplify working with Bayesian networks (BNs). Rather than modelling the variables in a problem domain, DNs represent the probability tables in the model. The graphical manipulation of the tables then takes on a biological feel, where a CPT $$P(X|Y)$$ is viewed as the novel representation of a population $$p(C,D)$$ using both combative traits $$C$$ (coloured clear) and docile traits $$D$$ (coloured dark).



DNs can unify modeling and reasoning tasks into a single platform. DNs can represent exact inference using either variable elimination or arc-reversal, lazy propagation , as well as how DNs can represent testing independencies. Adaptation and evolution are used to represent the testing of independencies and inference, respectively.