Started reading and commenting on Zolotov's paper on observations of a simulated galaxy. It shows that kinematics is not necessarily a good tracer of origin (that is, accreted versus in-situ formation for stars).
Suffering from insanity, I wrote a demo that uses Markov Chain Monte Carlo to fit a model composed of a mixture of k Gaussians to a set of one-dimensional data with proper Poisson likelihood. This is insane, because the Expectation-Maximization algorithm solves this problem already, and it is simple and comprehensible and far faster than MCMC. But I want us to be able to marginalize over parameters, so I need a sampling around the best fit. I don't think I have any other choice.
Maybe hybrid Monte Carlo would make you feel better?
ReplyDeletehttp://users.aims.ac.za/~mackay/itila/softwareB.html