  This paper introduces a new probabilistic architecture called Sum-Product
Graphical Model (SPGM). SPGMs combine traits from Sum-Product Networks (SPNs)
and Graphical Models (GMs): Like SPNs, SPGMs always enable tractable inference
using a class of models that incorporate context specific independence. Like
GMs, SPGMs provide a high-level model interpretation in terms of conditional
independence assumptions and corresponding factorizations. Thus, the new
architecture represents a class of probability distributions that combines, for
the first time, the semantics of graphical models with the evaluation
efficiency of SPNs. We also propose a novel algorithm for learning both the
structure and the parameters of SPGMs. A comparative empirical evaluation
demonstrates competitive performances of our approach in density estimation.
