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MapReduce for Bayesian Network Parameter Learning using the EM Algorithm
Proc. of Big Learning: Algorithms, Systems and Tools (2012)
  • Aniruddha Basak, Carnegie Mellon University
  • Irina Brinster, Carnegie Mellon University
  • Ole J Mengshoel, Carnegie Mellon University
Abstract
This work applies the distributed computing framework MapReduce to Bayesian network parameter learning from incomplete data. We formulate the classical Expectation Maximization (EM) algorithm within the MapReduce framework. Analytically and experimentally we analyze the speed-up that can be obtained by means of MapReduce. We present details of the MapReduce formulation of EM, report speed-ups versus the sequential case, and carefully compare various Hadoop cluster configurations in experiments with Bayesian networks of different sizes and structures.
Keywords
  • Bayesian network,
  • expectation maximization,
  • MapReduce,
  • Hadoop
Publication Date
December, 2012
Publisher Statement
@inproceedings{basak12mapreduce,
 author = {Basak, A. and Brinster, I. and Mengshoel, O. J.},
 title = {{MapReduce} for {Bayesian} Network Parameter Learning using the {EM} Algorithm},
 booktitle = {Proc. of Big Learning: Algorithms, Systems and Tools},
 year = {2012},
 month  = {December},
 address = {Lake Tahoe, NV}
}
Citation Information
Aniruddha Basak, Irina Brinster and Ole J Mengshoel. "MapReduce for Bayesian Network Parameter Learning using the EM Algorithm" Proc. of Big Learning: Algorithms, Systems and Tools (2012)
Available at: http://works.bepress.com/ole_mengshoel/38/