Artificial intelligence-assisted data analysis with BayesDB

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.

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2017

Author dc:creator

• Curlette, Christina M

Advisor dc:contributor.advisor

• Vikash K. Mansinghka.

• Electrical Engineering and Computer Science.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/119517

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/119517