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We consider a set x of discrete variables; each variable
xi has a finite set of values xi and a set of variables
pa(xi), the parents of xi. A Bayesian network
defines a unique joint probability distribution :
p(x) = prodi p(xi | pa(xi)).
We use the abbreviation pi for
p(xi|pa(xi)); expression (1) can be
written as p(x) = prodi pi.
Suppose a set of variables is fixed as evidence e;
pe() is a distribution where variables e are fixed.
Our algorithms assume efficient computation of posterior marginals in
a Bayesian network [12, 21, 40].
© Fabio Cozman[Send Mail?]
Fri May 30 15:55:18 EDT 1997