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Bayesian data assimilation for uncertainty quantification

Iglesias, MA (University of Warwick)
Wednesday 27 March 2013, 15:50-16:20

Seminar Room 1, Newton Institute


Modelling and simulation of physical systems require the especification of parameters which are often uncertain. In geophysical applications, for example, large uncertainty in model predictions arises from the lack of information of geologic properties. When observational data of the model dynamics are available, data assimilation techniques can be used to combine model and data to reduce and quantify the uncertainty. The quantification of uncertainty in predictions is essential for optimal design and decision making. In this talk I will discuss some general aspects of Bayesian data assimilation with applications to subsurface modelling.


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