Abstract:
Complexity reduction is an important task in Bayesian networks.
Recently, an approach known as the linear potential function (LPF)
model has been proposed for approximating Bayesian computations. The
LPF model can effectively compress a conditional probability table into a
linear function. This correspondence extends the LPF model to approximate
propagation in Bayesian networks. The extension focuses on encoding
probability propagation as a polynomial function for a class of tractable
problems.
