1. Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks.
- Author
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Ramos-López, Darío, Masegosa, Andrés R., Salmerón, Antonio, Rumí, Rafael, Langseth, Helge, Nielsen, Thomas D., and Madsen, Anders L.
- Subjects
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GAUSSIAN processes , *BAYESIAN analysis , *ESTIMATION theory , *STOCHASTIC analysis , *ALGORITHMS - 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 ). [ABSTRACT FROM AUTHOR]
- Published
- 2018
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