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Uncertainty Quantification in Complex, Nonparametric Statistical Models |
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Description and Aim Uncertainty quantification plays a central role in applications of
statistics to applied sciences such as physics, astronomy, geophysics,
epidemiology, genomics, etc. If one cannot quantify the accuracy of a
statistical procedure, a researcher has no information about the validity of
the inferences drawn from a particular method given a data set. A rigorous,
probabilistic description of statistical uncertainty quantification can be
based on the classical notion of a `confidence set', and its frequency interpretation
in the large sample/small noise limit. While the last decade has seen
remarkable progress in the mathematical theory of statistical confidence sets
in contemporary nonparametric and high-dimensional models, a gap remains
between theory and the methods used by practitioners (such as Bayesian credible
regions) in a range of modern statistical problems including structured data,
inverse problems and other models based on real world applications. The workshop aims to bring together different communities working on
uncertainty quantification. First there is a relatively clear distinction
between applied statisticians using uncertainty quantification in various
fields of sciences, computational statisticians considering algorithmic
aspects, and theoretical statisticians aiming on developing and underpinning
confidence statements. Furthermore, both frequentist and Bayesian methods are
widely used in practice and the workshop aims to bring together experts from
both fields. The workshop will be considered a success if it will lead to
valuable interaction between various views, if applied researchers and
computational statisticians will learn about the theoretical limits of UQ
methods and theoretical statisticians about the arising challenges in real world
applied problems and computationally feasible statistical techniques, which are
of emerging importance due to the ever increasing amount of available
information. The long term goal is to develop new, computationally feasible
statistical methods for uncertainty quantification in various fields of applied
sciences which have good theoretical properties at the same time. [Back] |
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