A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks
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چکیده
i=1 t i) k (87) We now comment on how the derivation changes when n = 2 and k 3. The case n 3 and k = 2 follows as well due to the symmetric functional equation (Equation 12). Note that up to Eq. 57 the derivation is valid when n = 2. Furthermore, note that the sum in Eq. 56 consists now of one term where l = j 1 = 1. Thus, Eq. 56 and Eq. 57 yield, using x i = z ij1 y j1 + z in y n (n=2, j 1 = 1), f i1 (W i1) j1 x i1 = z i1n y n
منابع مشابه
A Characterization of the Dirichlet Distribution with Application to Learning Bayesian Networks
We provide a new characterization of the Dirichlet distribution. This characterization implies that under assumptions made by sev eral previous authors for learning belief net works, a Dirichlet prior on the parameters is inevitable.
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