@inproceedings{38422a1c1696492184e17f420cb2b13f,
title = "Probabilistic constraints for inverse problems",
abstract = "The authors previous work on probabilistic constraint reasoning assumes the uncertainty of numerical variables within given bounds, characterized by a priori probability distributions. It propagates such knowledge through a network of constraints, reducing the uncertainty and providing a posteriori probability distributions. An inverse problem aims at estimating parameters from observed data, based on some underlying theory about a system behavior. This paper describes how nonlinear inverse problems can be cast into the probabilistic constraint framework, highlighting its ability to deal with all the uncertainty aspects of such problems.",
author = "Elsa Carvalho and Jorge Cruz and Pedro Barahona",
year = "2008",
doi = "10.1007/978-3-540-77664-2_10",
language = "English",
isbn = "978-3-540-77663-5",
volume = "46",
series = "Advances in Soft Computing",
publisher = "Springer",
pages = "115--128",
editor = "Van-Nam Huynh and Yoshiteru Nakamori and Hiroakira Ono and Jonathan Lawry and Vladik Kreinovich and Hung Nguyen",
booktitle = "Interval / Probabilistic Uncertainty and Non-Classical Logics",
address = "Netherlands",
note = "International Workshop on Interval and Probabilistic Uncertainty and Non-Classical Logics ; Conference date: 01-01-2008",
}