(Enter summary)
Abstract: this article is a characterization of
the Dirichlet distribution based on local and global parameter independence and on the
assumption that the probability distributions of all the parameters are positive pdfs. In
Section 3 we explore the circumstances under which our characterization implies that the
distribution of the parameters associated with each node in a Bayesian network must be
Dirichlet, in which case, the fifth assumption for learning is actually redundant. The as3
sumption of... (Update)
Context of citations to this paper: More
.... complete network structure dictates that the only possible prior parameter distribution for discrete DAG models is a Dirichlet prior [5, 7]. In contrast, in a subsequent work, it was shown that for Gaussian DAG models, which consist of a recursive set of linear regression...
.... with a strictly positive density, that satisfy certain natural mutual conditional indepen dence assumptions, is the Dirichlet distribution [GHe97]. Consequently, we assume that bik s are Dirichlet distributed ( ilk, bill, M) Di(kl, kM) 10) where ni s are global...
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BibTeX entry: (Update)
D. Geiger and D. Heckerman, A characterization of the Dirichlet distribution through local and global parameter independence. Submitted to Annals of Statistics, February 1995. http://citeseer.ist.psu.edu/article/geiger96characterization.html More
@techreport{ david94characterization,
author = "Heckerman, Dan Geiger David",
title = "{A} {C}haracterization of the {D}irichlet {D}istribution {T}hrough {G}lobal and {L}ocal {I}ndependence",
number = "MSR-TR-94-16",
month = "November",
year = "1994",
url = "citeseer.ist.psu.edu/article/geiger96characterization.html" }
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