  Sum-product networks have recently emerged as an attractive representation
due to their dual view as a special type of deep neural network with clear
semantics and a special type of probabilistic graphical model for which
inference is always tractable. Those properties follow from some conditions
(i.e., completeness and decomposability) that must be respected by the
structure of the network. As a result, it is not easy to specify a valid
sum-product network by hand and therefore structure learning techniques are
typically used in practice. This paper describes the first online structure
learning technique for continuous SPNs with Gaussian leaves. We also introduce
an accompanying new parameter learning technique.
