Bayesian optimization as a probabilistic meta-program
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2017
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| Online Access: | http://hdl.handle.net/1721.1/106374 |
| _version_ | 1826203853232537600 |
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| author | Zinberg, Ben (Ben I.) |
| author2 | Vikash K. Mansinghka. |
| author_facet | Vikash K. Mansinghka. Zinberg, Ben (Ben I.) |
| author_sort | Zinberg, Ben (Ben I.) |
| collection | MIT |
| description | Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2015. |
| first_indexed | 2024-09-23T12:44:39Z |
| format | Thesis |
| id | mit-1721.1/106374 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T12:44:39Z |
| publishDate | 2017 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1063742019-04-12T19:35:39Z Bayesian optimization as a probabilistic meta-program Zinberg, Ben (Ben I.) Vikash K. Mansinghka. 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, 2015. 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 50). This thesis answers two questions: 1. How should probabilistic programming languages in- corporate Gaussian processes? and 2. Is it possible to write a probabilistic meta-program for Bayesian optimization, a probabilistic meta-algorithm that can combine regression frameworks such as Gaussian processes with a broad class of parameter estimation and optimization techniques? We answer both questions affirmatively, presenting both an implementation and informal semantics for Gaussian process models in probabilistic programming systems, and a probabilistic meta-program for Bayesian optimization. The meta-program exposes modularity common to a wide range of Bayesian optimization methods in a way that is not apparent from their usual treatment in statistics. by Ben Zinberg. M. Eng. 2017-01-12T18:18:11Z 2017-01-12T18:18:11Z 2015 2015 Thesis http://hdl.handle.net/1721.1/106374 967346485 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 68 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Zinberg, Ben (Ben I.) Bayesian optimization as a probabilistic meta-program |
| title | Bayesian optimization as a probabilistic meta-program |
| title_full | Bayesian optimization as a probabilistic meta-program |
| title_fullStr | Bayesian optimization as a probabilistic meta-program |
| title_full_unstemmed | Bayesian optimization as a probabilistic meta-program |
| title_short | Bayesian optimization as a probabilistic meta-program |
| title_sort | bayesian optimization as a probabilistic meta program |
| topic | Electrical Engineering and Computer Science. |
| url | http://hdl.handle.net/1721.1/106374 |
| work_keys_str_mv | AT zinbergbenbeni bayesianoptimizationasaprobabilisticmetaprogram |