10th Applied Statistics 2013
International Conference
September 22 - 25, 2013
Ribno (Bled), Slovenia

Past conferences:
Applied Statistics 2012
Applied Statistics 2011

Applied Statistics 2010
Applied Statistics 2009
Applied Statistics 2008
Applied Statistics 2007

Applied Statistics 2006
Applied Statistics 2005
Applied Statistics 2004
Conferences before 2004


  Important dates

June 1

  Acceptance note
     June 15
Sent out

  Registration and
  hotel reservation
      July 1
  Reduced early
  payment fee
      July 15



Embedding Astronomical Computer Models into Principled Statistical Analyses

David A. van Dyk
Statistics Section Imperial College London, UK

Sophisticated and computational expensive computer models are routinely used to describe complex processes in the physical, engineering, and social sciences. In Astronomy, for example, computer models are used to describe such processes as the evolution of stars and galaxies, the formation of the Universe, and the workings and calibration of sophisticated space-based telescopes. Like a statistical likelihood, these models typically predict observed quantities as a function of a number of unknown parameters. Including them as components of a multi-level statistical model, however, leads to significant modeling, inferential, and computational challenges. In this talk, I describe how we tackle these challenges in the context of two examples: (i) a principled statistical analysis of stellar evolution and (ii) the calibration of space-based X-ray telescopes. In our stellar evolution model we must link together a number of separate computer models for various phases in the life of a star. These separate models are combined via parametric models the relate the output of one model with the inputs of the next and are themselves of direct scientific interest. In our calibration example, we employ computational simulators of the subassembly components of the detector to emulate the prior distribution of its response function. In both cases we embed the computer models into likelihood-based statistical models that allow for principled inference but present substantial computational challenges. We use a combination of sophisticated MCMC techniques and simple emulators of the computer models to tackle these challenges. This strategy allows us to apply the full force of powerful statistical tools to build, fit, check, and improve the statistical models and their computer model components. In contrast to traditional methods, we can estimate the uncertainty in our fit using principled statistical techniques, and we can ensure that the components of our overall statistical models are internally coherent.


 Applied Statistics 2013      http://conferences.nib.si/AS2013                                e-mail: info.AS@nib.si