  We present the general purpose spectral energy distribution (SED) fitting
tool SED Analysis Through Markov Chains (SATMC). Utilizing Monte Carlo Markov
Chain (MCMC) algorithms, SATMC fits an observed SED to SED templates or models
of the user's choice to infer intrinsic parameters, generate confidence levels
and produce the posterior parameter distribution. Here we describe the key
features of SATMC from the underlying MCMC engine to specific features for
handling SED fitting. We detail several test cases of SATMC, comparing results
obtained to traditional least-squares methods, which highlight its accuracy,
robustness and wide range of possible applications. We also present a sample of
submillimetre galaxies that have been fitted using the SED synthesis routine
GRASIL as input. In general, these SMGs are shown to occupy a large volume of
parameter space, particularly in regards to their star formation rates which
range from ~30-3000 M_sun yr^-1 and stellar masses which range from
~10^10-10^12 M_sun. Taking advantage of the Bayesian formalism inherent to
SATMC, we also show how the fitting results may change under different
parametrizations (i.e., different initial mass functions) and through
additional or improved photometry, the latter being crucial to the study of
high-redshift galaxies.
