  One of the fundamental goals of modern astrophysics is to estimate the
physical parameters of galaxies. We present a hierarchical Bayesian model to
compute age maps from images in the \Ha\ line (taken with Taurus Tunable
Filter, TTF), ultraviolet band (GALEX far UV, FUV), and infrared bands (Spitzer
24, 70, and 160 $\mu$m). We present the burst ages for young stellar
populations in a sample of nearby and nearly face-on galaxies. The \Ha\ to FUV
flux ratio is a good relative indicator of the very recent star formation
history (SFH). As a nascent star-forming region evolves, the \Ha\ line emission
declines earlier than the UV continuum, leading to a decrease in the \Ha/FUV
ratio. Using star-forming galaxy models, sampled with a probabilistic
formalism, and allowing for a variable fraction of ionizing photons in the
clusters, we obtain the corresponding theoretical ratio \Ha/FUV to compare with
our observed flux ratios, and thus to estimate the ages of the observed
regions. We take into account the mean uncertainties and the interrelationships
between parameters when computing \Ha/FUV. We propose a Bayesian hierarchical
model where a joint probability distribution is defined to determine the
parameters (age, metallicity, IMF) from the observed data (the observed flux
ratios \Ha/FUV). The joint distribution of the parameters is described through
independent and identically distributed (i.i.d.) random variables generated
through MCMC (Markov Chain Monte Carlo) techniques.
