Abstract
Sum-Product Networks (SPNs) are a probabilistic graphical model with deep learning applications. A key feature in an SPN is that inference is linear with respect to the size of the network under certain structural constraints. Initial studies of SPNs have investigated transforming SPNs into Bayesian Networks (BNs). Two such methods modify the SPN before conversion. One method modifies the SPN into a normal form. The resulting BN does not contain edges between latent variables. The other method considered here augments the SPN with twin nodes. Here, the constructed BN does contain edges between latent variables, thereby encoding a richer set of dependencies among them.
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