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We start by considering exact algorithms. Cano et all.
considered the same type of structures analyzed here, focusing more
on the axiomatic aspects of inference, and producing explicit
algorithms for networks with polytree topology [Cano, Delgado, & Moral1993].
Here we discuss how to generate exact algorithms for arbitrary networks
associated with finitely generated credal sets.
The technique we use to generate exact algorithms is based on a transformation
that operates on the Quasi-Bayesian network and produces a Bayesian
network. This transformation is central to our exact and
approximate algorithms.
Start from a network where some
variables are associated with credal sets. Call these variables zi.
For each variable zi, suppose its credal set has
mi vertices pi,j.
The basic transformation starts by modifying the original Bayesian network:
- For each variable zi associated with a credal set:
Figure 2 illustrates the basic transformation.
Call the variables z'i artificial variables.
The original network represents a finitely generated credal set, and each
vertex in this credal set can be obtained by setting the artificial
variables to arbitrary values. This is the crucial property of the
basic transformation.
Figure 2: The basic transformation for the
credal set associated with variable A
Consider a network with s credal sets, each credal set represented
by mi vertices. There are M = prodi=1s mi possible vertices in
the joint credal set.
Based on the previous discussion, we have an exact algorithm for
computation of posterior bounds as follows.
Algorithm- Initialize the lower and upper
bounds for the posterior marginals with ones and zeros respectively.
- Run any Bayesian inference
algorithm on the transformed network, for each one of the M
vertices of the original Quasi-Bayesian network. Each vertex is
generated by fixing the values of the artificial variables. For each
vertex, store the posterior marginal values that are smaller than the
lower bounds, and the posterior marginal values that are larger than the
upper bounds.
The algorithm has essentially
the same memory requirements as usual Bayesian inferences. The drawback
is that the algorithm does not use computations efficiently, wasting
calculations by repeating the same inferences again and again. To speed up
the algorithm, an implementation must cache parts of the network that
have no credal sets, for example by forming cliques of those parts and
caching intermediate results for such cliques. Based on this idea,
a new algorithm can be devised which which trades memory requirements
for speed. Instead of repeating computations, the following algorithm
performs a single Bayesian inference in the transformed network.
Algorithm- Run any Bayesian inference algorithm on the transformed network,
leaving the values of the artificial variables unspecified, such that
they form a single cluster. The result is
the conditional p(xq | { z'i }).
- Initialize the lower and upper
bounds for the posterior marginals with ones and zeros respectively.
- For all combinations of values
for the artificial variables,
store the posterior marginal values that are smaller than the
lower bounds, and the posterior marginal values that are larger than the
upper bounds.
Next: Complexity and generic approximations
Up: Robustness Analysis of Bayesian
Previous: Finitely generated convex sets
© Fabio Cozman[Send Mail?]
Tue Jan 21 15:59:56 EST 1997