Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks

Darío Ramos-López, Andrés Masegosa, Antonio Salmerón, Rafael Rumí, Helge Langseth, Thomas Dyhre Nielsen, Anders Læsø Madsen

Research output: Contribution to journalJournal articleResearchpeer-review

1 Citation (Scopus)

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).
Original languageEnglish
Article number100
JournalInternational Journal of Approximate Reasoning
Volume100
Pages (from-to)115-134
Number of pages20
ISSN0888-613X
DOIs
Publication statusPublished - 1 Sep 2018

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Importance sampling
Gaussian Mixture
Importance Sampling
Bayesian networks
Bayesian Networks
Stochastic Gradient
MapReduce
Ascent
Computing
Open Source
Learning systems
Machine Learning
Resources

Keywords

  • Bayesian networks
  • Conditional linear Gaussian models
  • Gaussian mixtures
  • Importance sampling
  • Scalable inference

Cite this

Ramos-López, Darío ; Masegosa, Andrés ; Salmerón, Antonio ; Rumí, Rafael ; Langseth, Helge ; Nielsen, Thomas Dyhre ; Madsen, Anders Læsø. / Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. In: International Journal of Approximate Reasoning. 2018 ; Vol. 100. pp. 115-134.
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Scalable importance sampling estimation of Gaussian mixture posteriors in Bayesian networks. / Ramos-López, Darío; Masegosa, Andrés; Salmerón, Antonio; Rumí, Rafael; Langseth, Helge; Nielsen, Thomas Dyhre; Madsen, Anders Læsø.

In: International Journal of Approximate Reasoning, Vol. 100, 100, 01.09.2018, p. 115-134.

Research output: Contribution to journalJournal articleResearchpeer-review

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