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...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2020
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| In: |
Machine learning
Year: 2019, Volume: 109, Issue: 1, Pages: 135-173 |
| ISSN: | 1573-0565 |
| DOI: | 10.1007/s10994-019-05813-2 |
| Online Access: | 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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| Author Notes: | Mattia Desana, Christoph Schnörr |
| Summary: | 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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| Item Description: | Published online: 27 June 2019 Gesehen am 27.02.2020 |
| Physical Description: | Online Resource |
| ISSN: | 1573-0565 |
| DOI: | 10.1007/s10994-019-05813-2 |