  We introduce a fast Markov Chain Monte Carlo (MCMC) exploration of the
astrophysical parameter space using a modified version of the publicly
available code CIGALE (Code Investigating GALaxy emission). The original CIGALE
builds a grid of theoretical Spectral Energy Distribution (SED) models and fits
to photometric fluxes from Ultraviolet (UV) to Infrared (IR) to put contraints
on parameters related to both formation and evolution of galaxies. Such a
grid-based method can lead to a long and challenging parameter extraction since
the computation time increases exponentially with the number of parameters
considered and results can be dependent on the density of sampling points,
which must be chosen in advance for each parameter. Markov Chain Monte Carlo
methods, on the other hand, scale approximately linearly with the number of
parameters, allowing a faster and more accurate exploration of the parameter
space by using a smaller number of efficiently chosen samples. We test our MCMC
version of the code CIGALE (called CIGALEMC) with simulated data. After
checking the ability of the code to retrieve the input parameters used to build
the mock sample, we fit theoretical SEDs to real data from the well known and
studied SINGS sample. We discuss constraints on the parameters and show the
advantages of our MCMC sampling method in terms of accuracy of the results and
optimization of CPU time.
