Scaling up Bayesian variational inference using distributed computing clusters

Andrés R. Masegosa*, Ana M. Martinez, Helge Langseth, Thomas Dyhre Nielsen, Antonio Salmerón, Darío Ramos-López, Anders Læsø Madsen

*Corresponding author

Research output: Contribution to journalJournal articleResearchpeer-review

7 Citations (Scopus)
Original languageEnglish
JournalInternational Journal of Approximate Reasoning
Volume88
Pages (from-to)435-451
Number of pages17
ISSN0888-613X
DOIs
Publication statusPublished - 1 Sep 2017

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Distributed computer systems
Distributed Computing
Variational Methods
Variational Bayes
Scaling
Bayesian Learning
MapReduce
Ascent
Evaluate
Exponential Family
Latent Variables
Factor Analysis
Linear Regression Model
Streaming
Open Source
Convergence Properties
Scalability
Machine Learning
Cover
Factor analysis

Keywords

  • Apache Flink
  • Conjugate exponential family
  • Probabilistic graphical models
  • Scalable Bayesian learning
  • Variational inference

Cite this

Masegosa, Andrés R. ; Martinez, Ana M. ; Langseth, Helge ; Nielsen, Thomas Dyhre ; Salmerón, Antonio ; Ramos-López, Darío ; Madsen, Anders Læsø. / Scaling up Bayesian variational inference using distributed computing clusters. In: International Journal of Approximate Reasoning. 2017 ; Vol. 88. pp. 435-451.
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Scaling up Bayesian variational inference using distributed computing clusters. / Masegosa, Andrés R.; Martinez, Ana M.; Langseth, Helge; Nielsen, Thomas Dyhre; Salmerón, Antonio; Ramos-López, Darío ; Madsen, Anders Læsø.

In: International Journal of Approximate Reasoning, Vol. 88, 01.09.2017, p. 435-451.

Research output: Contribution to journalJournal articleResearchpeer-review

TY - JOUR

T1 - Scaling up Bayesian variational inference using distributed computing clusters

AU - Masegosa, Andrés R.

AU - Martinez, Ana M.

AU - Langseth, Helge

AU - Nielsen, Thomas Dyhre

AU - Salmerón, Antonio

AU - Ramos-López, Darío

AU - Madsen, Anders Læsø

PY - 2017/9/1

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KW - Conjugate exponential family

KW - Probabilistic graphical models

KW - Scalable Bayesian learning

KW - Variational inference

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JF - International Journal of Approximate Reasoning

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