Probabilistic constraints for inverse problems

Elsa Carvalho, Jorge Cruz, Pedro Barahona

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationInterval / Probabilistic Uncertainty and Non-Classical Logics
EditorsVan-Nam Huynh, Yoshiteru Nakamori, Hiroakira Ono, Jonathan Lawry, Vladik Kreinovich, Hung Nguyen
Place of PublicationBerlin
Number of pages14
ISBN (Electronic)978-3-540-77664-2
ISBN (Print)978-3-540-77663-5
Publication statusPublished - 2008
EventInternational Workshop on Interval and Probabilistic Uncertainty and Non-Classical Logics -
Duration: 1 Jan 2008 → …

Publication series

NameAdvances in Soft Computing
ISSN (Print)1615-3871
ISSN (Electronic)1860-0794


ConferenceInternational Workshop on Interval and Probabilistic Uncertainty and Non-Classical Logics
Period1/01/08 → …


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