Issues with Bayesian Analysis of Inverse Problems
- Date: 03/19/2007
James Berger (Duke Univerisity)
University of British Columbia
A computer/math model of a process often contains numerous unknown
parameters. To utilize the model, these parameters either must be
determined from expert knowledge or estimated from data. In the
mathematical community, the most common methods of estimating such
parameters are through regularized best fit of various forms, and these
generally work fine in the presence of plentiful data. However, when
the data is limited, as is typically the case, parameter estimates
obtained in this way can be very uncertain and use of the model without
recognizing this uncertainty is quite problematic. Bayesian methods of
solving such inverse problems have been growing in popularity, because
of their ability to account for this uncertainty. A number of issues
and problems that arise in implementing Bayesian methods will be
discussed.
10th Anniversary Speaker Series 2007
