Sum-Product Graphical Models

Mattia Desana; Christoph Schnörr

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arXiv · 1708.06438

Sum-Product Graphical Models

Mattia Desana·Christoph Schnörr

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Abstract

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.

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BibTeX RIS

Mattia Desana, Christoph Schnörr. 2017-08-21. Sum-Product Graphical Models. https://arxiv.org/abs/1708.06438

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