Composable inference metaprogramming using subproblems
Name
1124924206-MIT.pdf
Size
4.5 MB
Format
Adobe PDF
Checksum (MD5)
0d0980a37fdb9362a9620d724b93be70
Author(s)
Handa, Shivam.
Advisor(s)
Martin Rinard.
Date Issued
2019
Publisher
Massachusetts Institute of Technology
Abstract
Inference metaprogramming enables effective probabilistic programming by supporting the decomposition of executions of probabilistic programs into subproblems and the deployment of hybrid probabilistic inference algorithms that apply different base probabilistic inference algorithms to different subproblems. I present the first sound and complete technique for extracting and stitching otherwise entangled subproblems for independent inference. I also prove asymptotic convergence results for hybrid inference algorithms for subproblem inference in probabilistic programs.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 139-142).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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