2026-09-18T20:47:31Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/978182022-01-13T07:54:01Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Armando Solar-Lezama.
Pu, Yewen
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2015-07-17T19:49:45Z
2015-07-17T19:49:45Z
2015
2015
http://hdl.handle.net/1721.1/97818
913221971
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).
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.
by Yewen Pu.
S.M.
58 pages
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
A novel inference algorithm on graphical model
Thesis
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