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Probabilistic constraints for inverse problems
2008
ECB08
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.
In proceedings
Elsa Carvalho, Jorge Cruz, Pedro Barahona
V. N. Huynh
Interval/Probabilistic Uncertainty and Non-classical Logics
Advances in Soft Computing
Springer
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46
115-128
978-3-540-77663-5
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-
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Elsa Carvalho and Jorge Cruz and Pedro Barahona, Probabilistic constraints for inverse problems, in: V. N. Huynh (eds), Interval/Probabilistic Uncertainty and Non-classical Logics, Advances in Soft Computing, Springer, Vol. 46, ISBN 978-3-540-77663-5, Pag. 115-128, 2008.
<a href="/people/members/view.php?code=8d1b2918d558af8e9308270b485b62a8" class="author">Elsa Carvalho</a>, <a href="/people/members/view.php?code=3f6f0c9973cdaeab1a3dd815682bb0ac" class="author">Jorge Cruz</a> and <a href="/people/members/view.php?code=7e27bc13fad97e99cd21ea6914d55659" class="author">Pedro Barahona</a>, <b>Probabilistic constraints for inverse problems</b>, in: V. N. Huynh (eds), <u>Interval/Probabilistic Uncertainty and Non-classical Logics</u>, Advances in Soft Computing, <a href="http://www.springer.com" title="Link to external entity..." target="_blank" class="publisher">Springer</a>, Vol. 46, ISBN 978-3-540-77663-5, Pag. 115-128, 2008.
@inproceedings {ECB08, author = {Elsa Carvalho and Jorge Cruz and Pedro Barahona}, editor = {V. N. Huynh}, title = {Probabilistic constraints for inverse problems}, booktitle = {Interval/Probabilistic Uncertainty and Non-classical Logics}, series = {Advances in Soft Computing}, publisher = {Springer}, volume = {46}, pages = {115-128}, isbn = {978-3-540-77663-5}, 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.}, year = {2008}, }
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