2026-09-19T06:55:01Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1195172026-06-06T00:49:06Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Vikash K. Mansinghka.
Curlette, Christina M
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2018-12-11T20:38:25Z
2018-12-11T20:38:25Z
2017
2017
http://hdl.handle.net/1721.1/119517
1066344990
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 (pages 67-68).
When applying machine learning and statistics techniques to real-world datasets, problems often arise due to missing data or errors from black-box predictive models that are difficult to understand or explain in terms of the model's inputs. This thesis explores the applicability of BayesDB, a probabilistic programming platform for data analysis, to three common problems in data analysis: (i) modeling patterns of missing data, (ii) imputing missing values in datasets, and (iii) characterizing the error behavior of predictive models. Experiments show that CrossCat, the default model discovery mechanism used by BayesDB, can address all three problems effectively. Examples are drawn from the American National Election Studies and the Gapminder database of global macroeconomic and public health indicators.
by Christina M. Curlette.
M.Eng.
68 pages
eng
Massachusetts Institute of Technology
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
Electrical Engineering and Computer Science.
Artificial intelligence-assisted data analysis with BayesDB
Thesis
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