and the model SED. The Student’s $t$-distribution log-likelihood does not depend solely on the data and its uncertainties, but also on the value of another variable, $\nu$, which controls the amount of weight on the tails of the Student’s $t$-distribution. For instance, if $\nu\rightarrow2$, the tails of the distribution have more weight in the distribution, whereas if $\nu\rightarrow + \infty$, the distribution tends to a normal distribution. We also model the value of $\nu$ during the log-likelihood mapping assuming a non-informative prior for the degrees-of-freedom given by

$$\nu\sim \textrm{Uniform}(2,50)\mbox{,}$$

which is required to be open-ended only in the lower bounds to avoid the undefined likelihood that occurs if $\nu=2$.

Sampling and results
--------------------

To deploy the above SED fitting modeling in the context of the project, we developed a Bayesian SED fitter (<span style="font-variant:small-caps;">BSF</span>, Barbosa, in prep.) as a general tool to model either SEDs or spectra of galaxies. <span style="font-variant:small-caps;">BSF</span> is written in the Python programming language based on the <span style="font-variant:small-caps;">pymc3</span> statistical package [@Salvatier2016], which allows for the construction of general Bayesian models while abstracting the complex issues related to the actual modeling and sampling. An attractive feature of the <span style="font-variant:small-caps;">pymc3</span> package, not found in other commonly 