2026-09-18T22:04:32Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1290782026-06-06T00:49:42Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Vikash K. Mansinghka.
Charchut, Nicholas George.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2021-01-06T17:38:39Z
2021-01-06T17:38:39Z
2020
2020
https://hdl.handle.net/1721.1/129078
1227274665
Thesis: M. Eng., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, September, 2020
Cataloged from student-submitted PDF of thesis.
Includes bibliographical references (page 83).
Understanding the underlying structure of a high-dimensional dataset is a quintessential task in data science and multivariate statistics. The CrossCat model class provides an automated solution by using Bayesian Non-Parametric processes to identify dependencies within the data without any user input necessary. This thesis provides an implementation of the CrossCat model class in the functional programming language Clojure. This implementation, called ClojureCat, was designed to be part of a probabilistic programming platform and implemented to be able to cross-compile into JavaScript, allowing complex inference procedures to be run in any JavaScript-supporting web browser. The implementation is thoroughly tested and benchmarked with respect to existing CrossCat implementations and other baselines, showing that ClojureCat is not only performant, but accurate in its implementation of CrossCat inference procedures. Also included in ClojureCat are several implementations of few-shot learning, in which for several real-world datasets, we utilize extremely sparse label sets and CrossCat's learned structure of the data to draw meaningful conclusions, make predictions, and further analyze the high-dimensional data.
by Nicholas George Charchut.
M.Eng.
M.Eng. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
83 pages
eng
Massachusetts Institute of Technology
MIT theses may be protected by copyright. Please reuse MIT thesis content according to the MIT Libraries Permissions Policy, which is available through the URL provided.
http://dspace.mit.edu/handle/1721.1/7582
Electrical Engineering and Computer Science.
Implementation of a cross-platform automated Bayesian data modeling system
Thesis
application/pdf
2021-01-06T17:38:39Z
Publication
Master
EECS
unknown
unknown
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<Abstract>Understanding the underlying structure of a high-dimensional dataset is a quintessential task in data science and multivariate statistics. The CrossCat model class provides an automated solution by using Bayesian Non-Parametric processes to identify dependencies within the data without any user input necessary. This thesis provides an implementation of the CrossCat model class in the functional programming language Clojure. This implementation, called ClojureCat, was designed to be part of a probabilistic programming platform and implemented to be able to cross-compile into JavaScript, allowing complex inference procedures to be run in any JavaScript-supporting web browser. The implementation is thoroughly tested and benchmarked with respect to existing CrossCat implementations and other baselines, showing that ClojureCat is not only performant, but accurate in its implementation of CrossCat inference procedures. Also included in ClojureCat are several implementations of few-shot learning, in which for several real-world datasets, we utilize extremely sparse label sets and CrossCat's learned structure of the data to draw meaningful conclusions, make predictions, and further analyze the high-dimensional data.</Abstract>
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