Bayesian nonparametric learning with semi-Markovian dynamics
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2010
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| Online Access: | http://hdl.handle.net/1721.1/60170 |
| _version_ | 1811084087075536896 |
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| author | Johnson, Matthew J., Ph. D. Massachusetts Institute of Technology (Matthew James) |
| author2 | Alan S. Willsky. |
| author_facet | Alan S. Willsky. Johnson, Matthew J., Ph. D. Massachusetts Institute of Technology (Matthew James) |
| author_sort | Johnson, Matthew J., Ph. D. Massachusetts Institute of Technology (Matthew James) |
| collection | MIT |
| description | Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. |
| first_indexed | 2024-09-23T12:44:09Z |
| format | Thesis |
| id | mit-1721.1/60170 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T12:44:09Z |
| publishDate | 2010 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/601702019-04-10T08:55:35Z Bayesian nonparametric learning with semi-Markovian dynamics Johnson, Matthew J., Ph. D. Massachusetts Institute of Technology (Matthew James) Alan S. Willsky. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Dept. of Electrical Engineering and Computer Science. Electrical Engineering and Computer Science. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010. Includes bibliographical references (p. 65-66). There is much interest in the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) as a natural Bayesian nonparametric extension of the ubiquitous Hidden Markov Model for learning from sequential and time-series data. However, in many settings the HDP-HMM's strict Markovian constraints are undesirable, particularly if we wish to learn or encode non-geometric state durations. We can extend the HDPHMM to capture such structure by drawing upon explicit-duration semi-Markovianity, which has been developed in the parametric setting to allow construction of highly interpretable models that admit natural prior information on state durations. In this thesis we introduce the explicit-duration Hierarchical Dirichlet Process Hidden semi-Markov Model (HDP-HSMM) and develop posterior sampling algorithms for efficient inference. We also develop novel sampling inference for the Bayesian version of the classical explicit-duration Hidden semi-Markov Model. We demonstrate the utility of the HDP-HSMM and our inference methods on synthetic data as well as experiments on a speaker diarization problem and an example of learning the patterns in Morse code. by Matthew J Johnson. S.M. 2010-12-06T17:32:55Z 2010-12-06T17:32:55Z 2010 2010 Thesis http://hdl.handle.net/1721.1/60170 681767340 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 66 p. application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Johnson, Matthew J., Ph. D. Massachusetts Institute of Technology (Matthew James) Bayesian nonparametric learning with semi-Markovian dynamics |
| title | Bayesian nonparametric learning with semi-Markovian dynamics |
| title_full | Bayesian nonparametric learning with semi-Markovian dynamics |
| title_fullStr | Bayesian nonparametric learning with semi-Markovian dynamics |
| title_full_unstemmed | Bayesian nonparametric learning with semi-Markovian dynamics |
| title_short | Bayesian nonparametric learning with semi-Markovian dynamics |
| title_sort | bayesian nonparametric learning with semi markovian dynamics |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/60170 |
| work_keys_str_mv | AT johnsonmatthewjphdmassachusettsinstituteoftechnologymatthewjames bayesiannonparametriclearningwithsemimarkoviandynamics |