Sum-Product Graphical Models
This work addresses the need for efficient and interpretable probabilistic models in machine learning, representing an incremental advancement by hybridizing existing methods.
The paper tackles the problem of combining the interpretability of graphical models with the tractable inference of sum-product networks by introducing Sum-Product Graphical Models (SPGMs), achieving competitive performance in density estimation.
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.