A neural network approach to MAP in belief networks

We suggest a neural network approach to probabilistic inference in Bayesian belief networks (BBN). This is demonstrated by solving maximum a posteriori probability (MAP) problems, which are known to be NP-hard. In this approach, a belief network is treated as a neural network without any structural changes, and the node activation functions are derived based on the probabilistic calculus of the BBN. Three models are proposed and their convergence analyzed. Computer experiments with two non-trivial example BBN show that this approach may lead to effective approximation methods for MAP