Composable inference metaprogramming using subproblems
Author(s)
Handa, Shivam.
Download1124924206-MIT.pdf (4.498Mb)
Other Contributors
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Advisor
Martin Rinard.
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Show full item recordAbstract
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).
Date issued
2019Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer SciencePublisher
Massachusetts Institute of Technology
Keywords
Electrical Engineering and Computer Science.