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Estimation Of Effects Of Sequential Treatments By Reparameterizing Directed Acyclic Graphs
- In D. Geiger and P. Shenoy (Eds.), Proceedings of the Thirteenth Conference on Uncertainty in Artificial Intelligence, Providence Rhode Island
, 1997
"... Introduction Consider a set of random variables V = (X 1 ; : : : ; XM ) whose joint density f (v) is represented by a Directed Acyclic Graph (DAG) G. If Pam represents the parents of Xm , then the density factorizes as f(v) = M Y m=1 f(xm jpa m ): (1) In practice, in order to estimate f (v) fr ..."
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Introduction Consider a set of random variables V = (X 1 ; : : : ; XM ) whose joint density f (v) is represented by a Directed Acyclic Graph (DAG) G. If Pam represents the parents of Xm , then the density factorizes as f(v) = M Y m=1 f(xm jpa m ): (1) In practice, in order to estimate f (v) from independent realizations V i ; i = 1; : : : ; n, obtained on n study subjects, one often needs to assume some particular parametric form for each f(xm jpa m ). Thus one writes f(v) = M Q m=1
