A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks (1995)
by
Dan Geiger
,
David Heckerman
| Citations: | 17 - 3 self |
BibTeX
@MISC{Geiger95acharacterization,
author = {Dan Geiger and David Heckerman},
title = {A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks},
year = {1995}
}
Years of Citing Articles
Bookmark
 |
 |
 |
 |
 |
 |
OpenURL
Abstract
this technical claim is that in order to find all positive integrable functions that satisfy Eq. 9, it is permissible to take any derivative at any point in the domain because it exists. By setting z ij = 1=k, for all i and j, in Equation 9 we get that f 0 (y 1 ; : : : ; y n\Gamma1 ) is proportional to
