At group meeting, Huppenkothen introduced us to methods for sampling "doubly intractable" Bayesian inference problems. The problem (and solution) in question is a variable-rate Poisson problem, where you have Poisson-distributed objects (like photons) arriving according to a mean rate that is varying with time, where that rate function is drawn from another process, in this case a Gaussian Process (taken through a function to make it non-negative). The best methods at the present day involve instantiating a lot of additional latent variables and then doing something like Gibbs sampling in the joint distribution of the parameters you care about and the newly introduced latent variables. We didn't understand everything about these complicated methods, but one of the authors, Iain Murray (Edinburgh) will be visiting the group next month, so we plan to make him talk.
Angus arrived for a few days of hacking and we talked about our super-Nyquist asteroseismology projects. We also started email conversations with the authors of this paper and this paper, both of which are impressive for their pedagogical presentation (as well as their results).