Sum-product graphical models
This paper introduces a probabilistic architecture called sum-product graphical model (SPGM). SPGMs represent a class of probability distributions that combines, for the first time, the semantics of probabilistic graphical models (GMs) with the evaluation efficiency of sum-product networks (SPNs): L...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2020
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| In: |
Machine learning
Year: 2019, Jahrgang: 109, Heft: 1, Pages: 135-173 |
| ISSN: | 1573-0565 |
| DOI: | 10.1007/s10994-019-05813-2 |
| Online-Zugang: | Resolving-System, Volltext: https://doi.org/10.1007/s10994-019-05813-2 Verlag: https://link.springer.com/article/10.1007%2Fs10994-019-05813-2 
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| Verfasserangaben: | Mattia Desana, Christoph Schnörr |
MARC
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| 520 | |a This paper introduces a probabilistic architecture called sum-product graphical model (SPGM). SPGMs represent a class of probability distributions that combines, for the first time, the semantics of probabilistic graphical models (GMs) with the evaluation efficiency of sum-product networks (SPNs): 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, this approach provides new connections between the fields of SPNs and GMs, and enables a high-level interpretation of the family of distributions encoded by SPNs. We provide two applications of SPGMs in density estimation with empirical results close to or surpassing state-of-the-art models. The theoretical and practical results demonstrate that jointly exploiting properties of SPNs and GMs is an interesting direction of future research. | ||
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