Variational inference for non-stationary distributions
Name
1017566873-MIT.pdf
Description
Full printable version
Size
2.5 MB
Format
Adobe PDF
Checksum (MD5)
a62d1dd93462dce52c5b9af922dd4086
Author(s)
Mamikonyan, Arsen
Advisor(s)
Samuel Madden.
Date Issued
2017
Publisher
Massachusetts Institute of Technology
Abstract
In this thesis, I look at multiple Variational Inference algorithm, transform Kalman Variational Bayes and Stochastic Variational Inference into streaming algorithms and try to identify if any of them work with non-stationary distributions. I conclude that Kalman Variational Bayes can do as good as any other algorithm for stationary distributions, and tracks non-stationary distributions better than any other algorithm in question.
Description
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 49).
Subjects
Electrical Engineering and Computer Science.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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