A method for knowledge integration

Martin Janžura; Pavel Boček

Kybernetika (1998)

  • Volume: 34, Issue: 1, page [41]-55
  • ISSN: 0023-5954

Abstract

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With the aid of Markov Chain Monte Carlo methods we can sample even from complex multi-dimensional distributions which cannot be exactly calculated. Thus, an application to the problem of knowledge integration (e. g. in expert systems) is straightforward.

How to cite

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Janžura, Martin, and Boček, Pavel. "A method for knowledge integration." Kybernetika 34.1 (1998): [41]-55. <http://eudml.org/doc/33333>.

@article{Janžura1998,
abstract = {With the aid of Markov Chain Monte Carlo methods we can sample even from complex multi-dimensional distributions which cannot be exactly calculated. Thus, an application to the problem of knowledge integration (e. g. in expert systems) is straightforward.},
author = {Janžura, Martin, Boček, Pavel},
journal = {Kybernetika},
keywords = {Markov chain Monte Carlo; multi-dimensional distribution; Gibbs distribution; sampled data; knowledge integration; expert systems; Markov chain Monte Carlo; multi-dimensional distribution; Gibbs distribution; sampled data; knowledge integration; expert systems},
language = {eng},
number = {1},
pages = {[41]-55},
publisher = {Institute of Information Theory and Automation AS CR},
title = {A method for knowledge integration},
url = {http://eudml.org/doc/33333},
volume = {34},
year = {1998},
}

TY - JOUR
AU - Janžura, Martin
AU - Boček, Pavel
TI - A method for knowledge integration
JO - Kybernetika
PY - 1998
PB - Institute of Information Theory and Automation AS CR
VL - 34
IS - 1
SP - [41]
EP - 55
AB - With the aid of Markov Chain Monte Carlo methods we can sample even from complex multi-dimensional distributions which cannot be exactly calculated. Thus, an application to the problem of knowledge integration (e. g. in expert systems) is straightforward.
LA - eng
KW - Markov chain Monte Carlo; multi-dimensional distribution; Gibbs distribution; sampled data; knowledge integration; expert systems; Markov chain Monte Carlo; multi-dimensional distribution; Gibbs distribution; sampled data; knowledge integration; expert systems
UR - http://eudml.org/doc/33333
ER -

References

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  8. Lauritzen S. L., Graphical Models, University Press, Oxford 1996 Zbl1055.62126MR1419991
  9. Matúš F., On iterations of average of I -projections, In: Highly Structured Stochastic Systems. Rebild, Denmark 1996 
  10. Moussouris J., 10.1007/BF01011714, J. Statist. Phys. 10 (1974), 1, 11–33 (1974) MR0432132DOI10.1007/BF01011714
  11. Perez A., Barycenter of a set of probability measures and its application in statistical decision, In: Proceedings COMPSTAT 1984, Physica–Verlag, Wien 1984, pp. 154–159 (1984) Zbl0577.62011MR0806993
  12. Winkler G., Image Analysis, Random Fields and Dynamic Monte Carlo Methods, Springer–Verlag, Berlin 1995 Zbl0821.68125MR1316400

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