  A fast and general Bayesian inference framework to infer the physical
properties of dichroic polarization using mid-infrared imaging- and
spectro-polarimetric observations is presented. The Bayesian approach is based
on a hierarchical regression and No-U-Turn Sampler method. This approach
simultaneously infers the normalized Stokes parameters to find the full family
of solutions that best describe the observations. In comparison with previous
methods, the developed Bayesian approach allows the user to introduce a
customized absorptive polarization component based on the dust composition, and
the appropriate extinction curve of the object. This approach allows the user
to obtain more precise estimations of the magnetic field strength and geometry
for tomographic studies, and information about the dominant polarization
components of the object. Based on this model, imaging-polarimetric
observations using two or three filters located in the central 9.5-10.5 $\mu$m,
and the edges 8-9 $\mu$m and/or 11-13 $\mu$m, of the wavelength range are
recommended to optimally disentangle the polarization mechanisms.
