Bayesian learning in sparse graphical factor models via variational mean-field annealing
Ryo Yoshida & Mike West

  • Publication: Journal of Machine Learning Research 2010, 11:1771-1798
  • Interested readers can visit the web site of the first author for supplementary material and code. This site contains general R software implementing the models and methods of the paper, together with data and complete information needed to recapitulate the example analyses reported. Also there is a additional supporting technical material in an earlier, extended technical report related to the theory and methods of the paper.


Elements of the research reported here were developed while Ryo Yoshida was visiting SAMSI and Duke University during 2008-09. Aspects of the research of Mike West were partially supported by grants from the U.S. National Science Foundation (DMS-0342172) and National Institutes of Health (NCI U54-CA-112952). Any opinions, findings and conclusions or recommendations expressed in this work are those of the authors and do not necessarily reflect the views of the NSF.


This software is made freely available to any interested user. The authors can provide no support nor assistance with implementations beyond the example in the code, nor extensions of the code for other purposes. The download has been tested to confirm all details are operational as described.

It is understood by the user that neither the authors nor Duke University bear any responsibility nor assume any liability for any end-use of this software. It is expected that appropriate credit/acknowledgment be given should the software be included as an element in other software development or in publications.


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