UAI 2021poster9 citations
Partial Identifiability in Discrete Data with Measurement Error
Noam Finkelstein, Roy Adams, Suchi Saria, Ilya Shpitser
Abstract
When data contains measurement errors, it is necessary to make modeling assumptions relating the error-prone measurements to the unobserved true values. Work on measurement error has largely focused on models that fully identify the parameter of interest. As a result, many practically useful models that result in
BibTeX
@InProceedings{pmlr-v161-finkelstein21b,
title = {Partial Identifiability in Discrete Data with Measurement Error},
author = {Finkelstein, Noam and Adams, Roy and Saria, Suchi and Shpitser, Ilya},
booktitle = {Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence},
pages = {1798--1808},
year = {2021},
editor = {de Campos, Cassio and Maathuis, Marloes H.},
volume = {161},
series = {Proceedings of Machine Learning Research},
month = {27--30 Jul},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v161/finkelstein21b/finkelstein21b.pdf},
url = {https://proceedings.mlr.press/v161/finkelstein21b.html},
abstract = {When data contains measurement errors, it is necessary to make modeling assumptions relating the error-prone measurements to the unobserved true values. Work on measurement error has largely focused on models that fully identify the parameter of interest. As a result, many practically useful models that result in