TR on Decimatable Boltzmann Machines vs. Gibbs Sampling

[Stefan M. Rueger]

                         Technical Report Available

             DECIMATABLE BOLTZMANN MACHINES VS. GIBBS SAMPLING

         Stefan M. Rueger, Anton Weinberger, and Sebastian Wittchen
                            Fachbereich Informatik
                        Technische Universitaet Berlin

                                  July 1996

  Exact Boltzmann learning can be done in certain restricted networks by
  the technique of decimation. We have enlarged the set of decimatable
  Boltzmann machines by introducing a new decimation rule. We have
  compared solutions of a probability density estimation problem with
  decimatable Boltzmann machines to the results obtained by Gibbs
  sampling in unrestricted (non-decimatable) Boltzmann machines.


This technical report is available in compressed Postscript by
the following URLs:

http://www.cs.tu-berlin.de/~async/www-pub/TR96-29.ps.gz
http://www.cs.tu-berlin.de/~async/www-pub/TR96-29-ds.ps.gz (2-on-1-page vers.)


Bibtex entry:

@TECHREPORT{TU-Berlin-Informatik-96-29,
  AUTHOR =    {Stefan M. R\"uger and Anton Weinberger and Sebastian Wittchen},
  TITLE  =    {Decimatable {B}oltzmann Machines vs. {G}ibbs Sampling},
  INSTITUTION={Fachbereich Informatik der Technischen Universit\"at Berlin},
  YEAR =      {1996},
  NUMBER =    {96-29}
}

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Stefan M. Rueger                             http://www.cs.tu-berlin.de/~async
Sekr. FR 5-9, Franklinstr. 28/29                         async at cs.tu-berlin.de
Technische Universitaet Berlin, 10 587 Berlin                 (+49)30/31422662
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