Stable Directed Belief Propagation in Gaussian DAGs using the auxiliary variable trick
David Barber and Peter Sollich
In: Neural Information Processing Systems 2005, 5-10 Dec 2005, Vancouver, Canada.
We consider approximate inference in the important class of large Gaussian distributions corresponding to multiply-connected directed acylic networks. We show how Directed Belief Propagation can be implemented in a numerically stable manner by associating backward (lambda) messages with an auxiliary variable, enabling intermediate computations to be carried out in moment form. We apply our method to the Fast Fourier Transform network with missing data, and show that the results are more accurate than those obtained using Undirected Belief Propagation on the equivalent pairwise Markov network.