Variational inference for non-stationary distributions
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/113125 |
| _version_ | 1826200809328607232 |
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| author | Mamikonyan, Arsen |
| author2 | Samuel Madden. |
| author_facet | Samuel Madden. Mamikonyan, Arsen |
| author_sort | Mamikonyan, Arsen |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2017. |
| first_indexed | 2024-09-23T11:42:08Z |
| format | Thesis |
| id | mit-1721.1/113125 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T11:42:08Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1131252019-04-11T09:25:33Z Variational inference for non-stationary distributions Mamikonyan, Arsen Samuel Madden. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. 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). 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. by Arsen Mamikonyan. M. Eng. 2018-01-12T20:57:47Z 2018-01-12T20:57:47Z 2017 2017 Thesis http://hdl.handle.net/1721.1/113125 1017566873 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 49 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Mamikonyan, Arsen Variational inference for non-stationary distributions |
| title | Variational inference for non-stationary distributions |
| title_full | Variational inference for non-stationary distributions |
| title_fullStr | Variational inference for non-stationary distributions |
| title_full_unstemmed | Variational inference for non-stationary distributions |
| title_short | Variational inference for non-stationary distributions |
| title_sort | variational inference for non stationary distributions |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/113125 |
| work_keys_str_mv | AT mamikonyanarsen variationalinferencefornonstationarydistributions |