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Approximating the Log-Partition Function

Author(s)
Cosson, Romain
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Advisor
Shah, Devavrat
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In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/
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Abstract
Graphical Models are used to represent structural information on a high-dimensional joint probability distribution. Their expressiveness offers simple reductions from a large number of NP-hard problems to inference tasks such as computing the partition function (exact inference) or approximating the log-partition function (approximate inference). In this master thesis, we will motivate the need for a general constant-factor approximations of the log-partition function and prove that a variant of the well studied tree-reweighted algorithm [1] achieves constant factor guarantees. We will express the corresponding approximation ratio 𝜅(𝐺) solely as a function of the graph structure 𝐺.
Date issued
2021-06
URI
https://hdl.handle.net/1721.1/139223
Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Publisher
Massachusetts Institute of Technology

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