The effect of loop length and count on the approximation error and convergence speed of loopy belief propagation

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dc.contributor.advisorDonselaar, Nils
dc.contributor.advisorKwisthout, Johan
dc.contributor.authorVerbeek, Janneke
dc.date.issued2020-06-19
dc.description.abstractPearl’s classical belief propagation does not always converge or give correct approximations of marginal probabilities when run on graphical models containing loops. Earlier investigations have seen it working well on single-loop graphical models as well as graphical models with multiple loops, but whether there is a direct relation between the number and length of the loops in graphical model and the accuracy and speed of convergence of LBP is not yet known. We found that increasing the number of loops in the graphical model decreases the convergence speed, while the accuracy of the marginal probabilities seems to decrease. Increasing the loop length of a single loop also decreases the convergence speed, but the accuracy of the marginal probabilities remains the same. Keywords: loopy belief propagation; factor graph; inference; approximation; loop length; loop count; cycle length; cycle count
dc.identifier.urihttps://theses.ubn.ru.nl/handle/123456789/15807
dc.language.isoen
dc.thesis.facultyFaculteit der Sociale Wetenschappen
dc.thesis.specialisationspecialisations::Faculteit der Sociale Wetenschappen::Artificial Intelligence::Bachelor Artificial Intelligence
dc.thesis.studyprogrammestudyprogrammes::Faculteit der Sociale Wetenschappen::Artificial Intelligence
dc.thesis.typeBachelor
dc.titleThe effect of loop length and count on the approximation error and convergence speed of loopy belief propagation
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