creator Zhang, Sisi advisor Kang, Lulu This paper studies how data uncertainties impact structure learning. Learning the structure of a probabilistic network from observational data has been traditionally studied assuming that there are no uncertainties in the data. This paper focuses on the uncertainties that result in “misclassification errors” in the contingency tables based on which the independence tests are carried out. The impact of misclassification errors is investigated through a sensitivity study which focuses on identifying the boundaries of misclassification errors within which the learned structure from erroneous data is identical to the true structure. Mathematical derivations for obtaining this boundary are presented. The analytical results are showed by a case study in epidemiology. Submitted by Liana Khananashvili (khananashvili@iit.edu) on 2014-12-19T20:00:02Z No. of bitstreams: 2 Learning the Structure of Probability Networks with Data Uncertainty.pdf: 365692 bytes, checksum: e5d863b807a2bdf035aef2b1ea18c5f2 (MD5) signed1.pdf: 14279226 bytes, checksum: 98392107c5b0e810f48b5342551f8246 (MD5) Made available in DSpace on 2014-12-19T20:00:02Z (GMT). No. of bitstreams: 2 Learning the Structure of Probability Networks with Data Uncertainty.pdf: 365692 bytes, checksum: e5d863b807a2bdf035aef2b1ea18c5f2 (MD5) signed1.pdf: 14279226 bytes, checksum: 98392107c5b0e810f48b5342551f8246 (MD5) Previous issue date: 2014-07 M.S. in Applied Mathematics, July 2014 2014 2014-07 http://hdl.handle.net/10560/3378 en Thesis born digital application/pdf In Copyright http://rightsstatements.org/page/InC/1.0/ Restricted Access MATH / Applied Mathematics Illinois Institute of Technology Affiliated department