adopted packages such as the <span style="font-variant:small-caps;">emcee</span> [@2013PASP..125..306F], is the implementation of the [, @2011arXiv1111.4246H], a Hamiltonian Monte Carlo sampler that has been shown to perform well in complex, multidimensional problems without the need of manual tuning. This sampler works better than other traditional samplers, such as the Metropolis-Hastings algorithm [@1970Bimka..57...97H] and the Gibbs sampler [@Gelfand1990], in problems with highly-correlated variables in a space parameter with hundreds of dimensions [see @2011arXiv1111.4246H].

As discussed in §\[sec:photometry\], there are a number of non-detections in our photometry. To simplify our modeling, we only included detected bands in the SED fitting for each UDG, and we leave the modeling including non-detections for forthcoming work. The samples from the posterior distributions were generated with <span style="font-variant:small-caps;">BSF</span> in four chains with 500 burn-in interactions and 500 draws. Figure \[fig:fitting\_images\] shows the comparison between the observations and the models for the sample of galaxies previously shown in Fig. \[fig:detection\_images\].

To illustrate the process of obtaining representative values and uncertainties for the model parameters, Fig. \[fig:corner\] shows the posterior samples determined with <span style="font-variant:small-caps;">BSF</span> for two of the presented in Fig. \[fig:detection\_images\]: SMDG0123079-002109, representing a relatively faint galaxy and SMDG0238220-011927 representing a relatively bright galaxy. Throughout our analysis, 