Apply priors on 1 and 2d histograms

Hi,
Not too much of an issue here, more of an additional feature.
The idea is to multiply the 1d and 2d histograms curves/surfaces with the respective prior of each parameter, so that the full "posterior distribution" is plotted - not just the likelihood of the raw samples.

It seemed that the "weights" arguments served another purpose, so I hacked something together. It actually holds in a tiny amount of lines.
Only one new input argument "priors" is needed. It is a N-list of (callable, dict) (for N parameters). The callable argument being the function to call with the dict as *_kwargs:
prior_prob = callable(x, *_dict).
The only drawback is the loss of the parameter "histtype" (hist_kwargs["histtype"]) for the 1d histograms, since they are now always plotted with ax.plot(...) whether they have smoothing or not.
Cheers,
G. Schworer @ceyzeriat

Start by locating the 1D and 2D histogram plotting entry points and reviewing how their current weights arguments are handled. Add support for the proposed priors list of callable-and-dict pairs, and verify that both histogram curves and surfaces represent the posterior rather than only the likelihood; check the documented histtype limitation for 1D plots.