Typesafety for explicitly-coded probabilistic inference procedures
Name
1036986659-MIT.pdf
Description
Full printable version
Size
4.46 MB
Format
Adobe PDF
Checksum (MD5)
35661d8c2ff113b7d55d7338c414511b
Author(s)
Atkinson, Eric Hamilton
Advisor(s)
Michael Carbin.
Date Issued
2018
Publisher
Massachusetts Institute of Technology
Abstract
Researchers have recently proposed several systems that ease the process of developing Bayesian probabilistic inference algorithms. These include systems for automatic inference algorithm synthesis as well as stronger abstractions for manual algorithm development. However, existing systems whose performance relies on the developer manually constructing a part of the inference algorithm have limited support for reasoning about the correctness of the resulting algorithm. In this thesis, I present Shuffle, a programming language for developing manual inference algorithms that enforces 1) the basic rules of probability theory and 2) statistical dependencies of the algorithm's corresponding probabilistic model. We have used Shuffle to develop inference algorithms for several standard probabilistic models. Our results demonstrate that Shuffle enables a developer to deliver performant implementations of these algorithms with the added benefit of Shuffle's correctness guarantees.
Description
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 67-68).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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