This paper studies how data uncertainties impact structure learning. Learning the structure of a probabilistic network from observational data... Show moreThis 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. M.S. in Applied Mathematics, July 2014 Show less
