A comparison of feedback vertex set and loop cutset algorithms as preprocessing of conditioned polytree algorithm
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2022-01-29
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en
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Abstract
The poly-tree algorithm (Pearl’s algorithm) is an exact probabilistic inference
method that updates messages from node to node until the marginal
probability distribution of each node is consistent with the evidence [15].
The algorithm only works on singly connected networks. A possible way
around this restriction is using conditioning algorithms to reduce the graph
structure to obtain a singly connected network. To study the relative performance
of exact versus heuristic approaches, I used one exact conditioning,
loop cut-set, and one inexact conditioning method, feedback vertex set.
Comparing the resulting performance measures and marginal probability
distributions, I found that loop cut-set conditioning algorithm is preferred
over the feedback vertex set conditioning in smaller networks and FVS had
surprising accuracy in marginal dependencies despite a significant decrease
in time required in larger networks. The varying number of loops merely
affected the convergence rate performance of the loop cut-set in smaller networks.
FVS heuristic conditioning recovered marginal probabilities better
in larger networks compared to its performance on smaller networks. It
performed faster and resulted in similar accuracy of the loop cut-set conditioning.
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Faculteit der Sociale Wetenschappen