  We introduce factorize sum split product networks (FSPNs), a new class of
probabilistic graphical models (PGMs). FSPNs are designed to overcome the
drawbacks of existing PGMs in terms of estimation accuracy and inference
efficiency. Specifically, Bayesian networks (BNs) have low inference speed and
performance of tree structured sum product networks(SPNs) significantly
degrades in presence of highly correlated variables. FSPNs absorb their
advantages by adaptively modeling the joint distribution of variables according
to their dependence degree, so that one can simultaneously attain the two
desirable goals: high estimation accuracy and fast inference speed. We present
efficient probability inference and structure learning algorithms for FSPNs,
along with a theoretical analysis and extensive evaluation evidence. Our
experimental results on synthetic and benchmark datasets indicate the
superiority of FSPN over other PGMs.
