Parallel Importance Sampling in Conditional Linear Gaussian Networks
In this paper we analyse the problem of probabilistic inference in CLG networks when evidence comes in streams. In such situations, fast and scalable algorithms, able to provide accurate responses in a short time are required. We consider the instantiation of variational inference and importance s...
| Main Authors: | , , , , , , , , |
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| Format: | info:eu-repo/semantics/article |
| Language: | English |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/10835/4858 |
| _version_ | 1789406726905135104 |
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| author | Salmerón Cerdán, Antonio Ramos López, Darío Borchani, Hanen Martínez, Ana M. Masegosa, Andrés R. Fernández, Antonio Langseth, Helge Madsen, Anders L. Nielsen, Thomas D. |
| author_facet | Salmerón Cerdán, Antonio Ramos López, Darío Borchani, Hanen Martínez, Ana M. Masegosa, Andrés R. Fernández, Antonio Langseth, Helge Madsen, Anders L. Nielsen, Thomas D. |
| author_sort | Salmerón Cerdán, Antonio |
| collection | DSpace |
| description | In this paper we analyse the problem of probabilistic inference in CLG networks when evidence comes in streams. In such situations, fast and scalable algorithms, able to provide accurate responses in
a short time are required. We consider the instantiation of variational
inference and importance sampling, two well known tools for probabilistic inference, to the CLG case. The experimental results over synthetic
networks show how a parallel version importance sampling, and more
precisely evidence weighting, is a promising scheme, as it is accurate and
scales up with respect to available computing resources. |
| format | info:eu-repo/semantics/article |
| id | oai:repositorio.ual.es:10835-4858 |
| institution | Universidad de Cuenca |
| language | English |
| publishDate | 2017 |
| record_format | dspace |
| spelling | oai:repositorio.ual.es:10835-48582023-04-12T19:40:26Z Parallel Importance Sampling in Conditional Linear Gaussian Networks Salmerón Cerdán, Antonio Ramos López, Darío Borchani, Hanen Martínez, Ana M. Masegosa, Andrés R. Fernández, Antonio Langseth, Helge Madsen, Anders L. Nielsen, Thomas D. In this paper we analyse the problem of probabilistic inference in CLG networks when evidence comes in streams. In such situations, fast and scalable algorithms, able to provide accurate responses in a short time are required. We consider the instantiation of variational inference and importance sampling, two well known tools for probabilistic inference, to the CLG case. The experimental results over synthetic networks show how a parallel version importance sampling, and more precisely evidence weighting, is a promising scheme, as it is accurate and scales up with respect to available computing resources. 2017-06-14T09:57:01Z 2017-06-14T09:57:01Z 2015 info:eu-repo/semantics/article http://hdl.handle.net/10835/4858 en Attribution-NonCommercial-NoDerivatives 4.0 Internacional http://creativecommons.org/licenses/by-nc-nd/4.0/ info:eu-repo/semantics/openAccess |
| spellingShingle | Salmerón Cerdán, Antonio Ramos López, Darío Borchani, Hanen Martínez, Ana M. Masegosa, Andrés R. Fernández, Antonio Langseth, Helge Madsen, Anders L. Nielsen, Thomas D. Parallel Importance Sampling in Conditional Linear Gaussian Networks |
| title | Parallel Importance Sampling in Conditional Linear Gaussian Networks |
| title_full | Parallel Importance Sampling in Conditional Linear Gaussian Networks |
| title_fullStr | Parallel Importance Sampling in Conditional Linear Gaussian Networks |
| title_full_unstemmed | Parallel Importance Sampling in Conditional Linear Gaussian Networks |
| title_short | Parallel Importance Sampling in Conditional Linear Gaussian Networks |
| title_sort | parallel importance sampling in conditional linear gaussian networks |
| url | http://hdl.handle.net/10835/4858 |
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