 
Summary: Inference Using Message Propagation and Topology Transformation
in Vector Gaussian Continuous Networks
Satnam Alag Alice M. Agogino
Department of Mechanical Engineering Department of Mechanical Engineering
University of California at Berkeley University of California at Berkeley
alag@pawn.berkeley.edu aagogino@euler.me.berkeley.edu
Abstract
We extend continuous Gaussian networks  directed
acyclic graphs that encode probabilistic
relationships between variables  to its vector form.
Vector Gaussian continuous networks consist of
composite nodes representing multivariables, that
take continuous values. These vector or composite
nodes can represent correlations between parents, as
opposed to conventional univariate nodes. We
derive rules for inference in these networks based on
two methods: message propagation and topology
transformation. These two approaches lead to the
development of algorithms, that can be implemented
in either a centralized or a decentralized manner.
