On the way up to Columbia to chat with Johnston, Sanderson and I discussed the probabilistic interpretation of our information-theory approach to Milky Way streams. I argued that we should frame this discussion entirely in terms of likelihood functions, and give up on posterior pdfs. The main reason is that, in my view, "information" is a property of a likelihood function, really. It is the likelihood that moves information from the data to the quantities of interest. Besides, our job as scientists is to provide likelihood functions!
At Columbia, Price-Whelan showed me cuts through the likelihood function for our generative probabilistic model of stream stars. The MCMC sampling is (not surprisingly) turning out to be hard; one of my go-to moves in this case is to check that the likelihood function is smooth and peaked where it should be (and not elsewhere as well). All tests pass, so I think we are down to improving the initialization and burn-in of emcee.