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Published Articles >> Table of Contents >> Abstract
18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06)
pp. 455-464
A Junction Tree Propagation Algorithm for Bayesian Networks with Second-Order Uncertainties
Maurizio Borsotto, GCAS Incorporated, USA
Weihong Zhang, GCAS Incorporated, USA
Emir Kapanci, Harvard University, USA
Avi Pfeffer, Harvard University, USA
Christopher Crick, Yale University, USA
Full Article Text:
 
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICTAI.2006.14
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| Abstract |
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Bayesian networks (BNs) have been widely used as a
model for knowledge representation and probabilistic inferences.
However, the single probability representation
of conditional dependencies has been proven to be overconstrained
in realistic applications. Many efforts have
proposed to represent the dependencies using probability
intervals instead of single probabilities. In this paper, we
move one step further and adopt a probability distribution
schema. This results in a higher order representation of uncertainties
in a BN.We formulate probabilistic inferences in
this context and then propose a mean/covariance propagation
algorithm based on the well-known junction tree propagation
for standard BNs [1]. For algorithm validation,
we develop a two-layered Markov likelihood weighting approach
that handles high-order uncertainties and provides
"ground-truth" solutions to inferences, albeit very slowly.
Our experiments show that the mean/covariance propagation
algorithm can efficiently produce high-quality solutions
that compare favorably to results obtained through
painstaking sampling.
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 Additional Information
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Citation:
Maurizio Borsotto, Weihong Zhang, Emir Kapanci, Avi Pfeffer, Christopher Crick,
"A Junction Tree Propagation Algorithm for Bayesian Networks with Second-Order Uncertainties,"
ictai,
pp. 455-464,
18th IEEE International Conference on Tools with Artificial Intelligence (ICTAI'06),
2006
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