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Maximum Entropy and Bayesian Methods pp 61–68Cite as

A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks

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Part of the book series: Fundamental Theories of Physics ((FTPH,volume 79))

Abstract

We provide a new characterization of the Dirichlet distribution. This characterization implies that under assumptions made by several previous authors for learning belief networks, a Dirichlet prior on the parameters is inevitable.

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References

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Author information

Authors and Affiliations

  1. Computer Science Department, Technion, Haifa, 32000, Israel

    Dan Geiger

  2. Microsoft Research, 9S, Redmond, WA, 98052-6399, USA

    David Heckerman

Authors
  1. Dan Geiger
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  2. David Heckerman
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Editor information

Editors and Affiliations

  1. Dynamic Experimentation Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA

    Kenneth M. Hanson

  2. Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico, USA

    Richard N. Silver

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© 1996 Springer Science+Business Media Dordrecht

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Geiger, D., Heckerman, D. (1996). A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks. In: Hanson, K.M., Silver, R.N. (eds) Maximum Entropy and Bayesian Methods. Fundamental Theories of Physics, vol 79. Springer, Dordrecht. https://doi.org/10.1007/978-94-011-5430-7_7

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  • DOI: https://doi.org/10.1007/978-94-011-5430-7_7

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-010-6284-8

  • Online ISBN: 978-94-011-5430-7

  • eBook Packages: Springer Book Archive

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