I've been working for a while on a compiler for statistical models. (Those of you who knew me back in the day will know that I used to be a programming languages/compiler nut.) The basic idea is to have a language in which you can naturally express hierarchical Bayesian models and either simulate them directly or compile them into your language of choice (in this case, right now, the only language you can choose in C :P). The key differentiation between HBC (the Hierarchical Bayes Compiler) and tools like WinBugs is that you're actually supposed to be able to use HBC to do large-scale simulations. Also, although right now it only supports Gibbs sampling, message passing and stochastic EM variants are in the works. It also almost knows how to automatically collapse out variables (i.e., automatic Rao-Blackwellization), but this is still a bit buggy.
Anyway, you can see more information on the official website. I'd really appreciate people who are interested in sending me bug reports if you encounter any problems. It's a pretty complex bit of machinery and some of it is still hacked together rather than done properly (especially type checking), so I expect to find some bugs there.
So just to whet your appetite if you haven't yet clicked on the above link, here's a complete implementation of LDA including hyperparameter estimation in HBC:
alpha ~ Gam(0.1,1)
eta ~ Gam(0.1,1)
beta_{k} ~ DirSym(eta, V) , k \in [1,K]
theta_{d} ~ DirSym(alpha, K) , d \in [1,D]
z_{d,n} ~ Mult(theta_{d}) , d \in [1,D] , n \in [1,N_{d}]
w_{d,n} ~ Mult(beta_{z_{d,n}}) , d \in [1,D] , n \in [1,N_{d}]
