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We introduce a novel image decomposition package, GALPHAT, that providesrobust estimates of galaxy surface brightness profiles using Bayesian MarkovChain Monte Carlo. The GALPHAT-determined posterior distribution of parametersenables us to assign rigorous statistical confidence intervals to maximum aposteriori estimates and to test complex galaxy formation and evolutionhypotheses. We describe the GALPHAT algorithm, assess its performance usingtest image data, and demonstrate that it has sufficient speed for productionanalysis of a large galaxy sample. Finally we briefly introduce our ongoingscience program to study the distribution of galaxy structural properties inthe local universe using GALPHAT.

Beyond the best-fit parameter: new insight on galaxy structure decomposition from GALPHAT