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A Characterization of the Dirichlet Distribution through Global and Local Parameter Independence (1996)  (Make Corrections)  (12 citations)
Dan Geiger, David Heckerman



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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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