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Model Uncertainty: Mathematical and Statistical (MUMS)

This is a year-long cross-disciplinary research program starting August 2018 at the Statistical and Mathematical Sciences Institute (SAMSI) in Research Triangle NC USA. It is rarely the case that either a mathematical model or a statistical model of a process can be constructed, with assurance from its construction that the model accurately represents or predicts the complete process. Thus, in the crucial final step of utilizing the model for prediction or understanding of the real-world process, it is of central importance to understand the uncertainties inherent in using the model. In mathematics and engineering, this is called Uncertainty Quantification (UQ) and has become a major part of applied mathematics. In statistics this is called Model Uncertainty (MU), and has long been one of the most prominent fields of statistics, and includes hypothesis testing, model selection, model averaging, model criticism, and many other specialty areas. UQ and MU have developed mostly independently and with almost completely separate communities. Bringing together researchers from the two communities to attack these common goals is the primary goal of this SAMSI program.
When 20 August 2018 12:45 PM to
16 May 2019 12:45 PM
Where Research Triangle NC USA
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Tentative Working Groups for this program:

  1. Identifying and Attacking Model Uncertainty Commonalities.
  2. Modeling Across Multiple Scales and Disciplines.
  3. Biomedical Data and Precision Medicine.
  4. Materials Informatics and Mechanics.
  5. Stochastic Discretization.
  6. Quantifying Uncertainty in Extrapolative Settings.
  7. Foundations of Statistical Model Uncertainty.

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