Abstract
The probabilistic continuous constraint (PC) framework complements the representation of uncertainty by means of intervals with a probabilistic distribution of values within such intervals. This paper, published in Constraints [8], describes how nonlinear inverse problems can be cast into this framework, highlighting its ability to deal with all the uncertainty aspects of such problems, and illustrates this new methodology in Ocean Color (OC), a research area widely used in climate change studies with significant applications in water quality monitoring.
This is a summary of the paper: E. Carvalho, J. Cruz, and Pedro Barahona. Probabilistic constraints for nonlinear inverse problems. Constraints, 18(3):344-376, 2013.
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Carvalho, E., Cruz, J., Barahona, P. (2014). Probabilistic Constraints for Nonlinear Inverse Problems. In: O’Sullivan, B. (eds) Principles and Practice of Constraint Programming. CP 2014. Lecture Notes in Computer Science, vol 8656. Springer, Cham. https://doi.org/10.1007/978-3-319-10428-7_66
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DOI: https://doi.org/10.1007/978-3-319-10428-7_66
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