dc.contributor.author | Ramos-López, Dario | |
dc.contributor.author | Masegosa, Andres | |
dc.contributor.author | Salmeron, Antonio | |
dc.contributor.author | Rumi, Rafael | |
dc.contributor.author | Langseth, Helge | |
dc.contributor.author | Nielsen, Thomas D. | |
dc.contributor.author | Madsen, Anders L. | |
dc.date.accessioned | 2019-04-30T07:47:34Z | |
dc.date.available | 2019-04-30T07:47:34Z | |
dc.date.created | 2018-07-29T09:43:38Z | |
dc.date.issued | 2018 | |
dc.identifier.citation | International Journal of Approximate Reasoning. 2018, 100 115-134. | nb_NO |
dc.identifier.issn | 0888-613X | |
dc.identifier.uri | http://hdl.handle.net/11250/2596038 | |
dc.description.abstract | In this paper we propose a scalable importance sampling algorithm for computing Gaussian mixture posteriors in conditional linear Gaussian Bayesian networks. Our contribution is based on using a stochastic gradient ascent procedure taking as input a stream of importance sampling weights, so that a mixture of Gaussians is dynamically updated with no need to store the full sample. The algorithm has been designed following a Map/Reduce approach and is therefore scalable with respect to computing resources. The implementation of the proposed algorithm is available as part of the AMIDST open-source toolbox for scalable probabilistic machine learning (http://www.amidsttoolbox.com). | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Elsevier | nb_NO |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/deed.no | * |
dc.title | Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks | nb_NO |
dc.title.alternative | Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 115-134 | nb_NO |
dc.source.volume | 100 | nb_NO |
dc.source.journal | International Journal of Approximate Reasoning | nb_NO |
dc.identifier.doi | 10.1016/j.ijar.2018.06.004 | |
dc.identifier.cristin | 1598880 | |
dc.description.localcode | © 2018. This is the authors’ accepted and refereed manuscript to the article. Locked until 13.6.2020 due to copyright restrictions. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |