Beyond the best-fit parameter: new insight on galaxy structure
  decomposition from GALPHAT
  We introduce a novel image decomposition package, GALPHAT, that provides
robust estimates of galaxy surface brightness profiles using Bayesian Markov
Chain Monte Carlo. The GALPHAT-determined posterior distribution of parameters
enables us to assign rigorous statistical confidence intervals to maximum a
posteriori estimates and to test complex galaxy formation and evolution
hypotheses. We describe the GALPHAT algorithm, assess its performance using
test image data, and demonstrate that it has sufficient speed for production
analysis of a large galaxy sample. Finally we briefly introduce our ongoing
science program to study the distribution of galaxy structural properties in
the local universe using GALPHAT.
