Titlebook: Introduction to Uncertainty Quantification; T.J. Sullivan Textbook 2015 Springer International Publishing Switzerland 2015 Computational p

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Distributional Uncertainty,out what the real distributions actually are. The same is true of uncertainty about the correct form of the forward physical model. In the Bayesian paradigm, similar issues arise if the available information is insufficient for us to specify (or ‘elicit’) a unique prior and likelihood model.

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Bayesian Inverse Problems,on. One specific aim is to make clear the connection between regularization and the application of a Bayesian prior. The filtering methods of Chapter . fall under the general umbrella of Bayesian approaches to inverse problems, but have an additional emphasis on real-time computational expediency.

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Numerical Integration,endent variables, i.e. high-dimensional domains of integration. Hence, the accurate numerical integration of integrands over high-dimensional spaces using few samples is something of a ‘Holy Grail’ in this area.

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