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SUMMARY:Dynamic discretization of inverse problems using hierarchical Baye
 sian models - Erkki Somersalo (Case Western Reserve University)
DTSTART:20230622T090000Z
DTEND:20230622T100000Z
UID:TALK200446@talks.cam.ac.uk
DESCRIPTION:Estimating distributed parameters from indirect noisy observat
 ions requires a discretization of the unknown quantity to make the forward
  model computationally feasible. In ill-posed problems\, the modeling erro
 r due to the discretization may have an adverse impact on the solution if 
 not taken into account properly\, in particular\, when the&nbsp\; quality 
 of the data is high and the modeling error dominates the noise. To minimiz
 e the effect of the modeling error\, refinement of the discretization is a
 n option that may increase significantly the computational cost. Computati
 onal efficiency may be increased by selectively refining the discretizatio
 n only where needed\, and by using anisotropic discretization. In this tal
 k\, the problem is addressed by defining the discretization in terms of a 
 metric that is coupled to the unknown distributed parameter through a Baye
 sian hypermodel\, thus making the discretization part of the inverse probl
 em. Computed examples of this coupled problem include a sparse view tomogr
 aphy problem.
LOCATION:Seminar Room 1\, Newton Institute
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