A novel inference algorithm on graphical model
Name
913221971-MIT.pdf
Description
Full printable version
Size
2.24 MB
Format
Adobe PDF
Checksum (MD5)
c4fc829c546304c822b93397a97eb0c6
Author(s)
Pu, Yewen
Advisor(s)
Armando Solar-Lezama.
Date Issued
2015
Publisher
Massachusetts Institute of Technology
Abstract
We present a framework for approximate inference that, given a factor graph and a subset of its variables, produces an approximate marginal distribution over these variables with bounds. The factors of the factor graph are abstracted as as piecewise polynomial functions with lower and upper bounds, and a variant of the variable elimination algorithm solves the inference problem over this abstraction. The resulting distributions bound quantifies the error between it and the true distribution. We also give a set of heuristics for improving the bounds by further refining the binary space partition trees.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 57-58).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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