## HBC: Hierarchical Bayes Compiler

##### Pre-release version 0.1
HBC is a toolkit for implementing hierarchical Bayesian models. HBC created because I felt like I spend too much time writing boilerplate code for inference problems in Bayesian models. There are several goals of HBC:
1. Allow a natural implementation of hierarchal models.
2. Enable quick and dirty debugging of models for standard data types.
3. Focus on large-dimension discrete models.
4. More general that simple Gibbs sampling (eg., allowing for maximizations, EM and message passing).
5. Allow for hierarchical models to be easily embedded in larger programs.
6. Automatic Rao-Blackwellization (aka collapsing).
7. Allow efficient execution via compilation to other languages (such as C, Java, Matlab, etc.).
These goals distinguish HBC from other Bayesian modeling software, such as Bugs (or WinBugs). In particular, our primary goal is that models created in HBC can be used directly, rather than only as a first-pass test. Moreover, we aim for scalability with respect to data size. Finally, since the goal of HBC is to compile hierarchical models into standard programming languages (like C), these models can easily be used as part of a larger system. This last point is in the spirit of the dynamic programming language Dyna.

Note that some of these aren't yet supported (in particular: 4 and 6) but should be coming soon!

### A Quick Example

To give a flavor of what HBC is all about, here is a complete implementation of a Bayesian mixture of Gaussians model in HBC format:
  alpha     ~ Gam(10,10)
mu_{k}    ~ NorMV(vec(0.0,1,dim), 1)     , k \in [1,K]
si2       ~ IG(10,10)
pi        ~ DirSym(alpha, K)
z_{n}     ~ Mult(pi)                     , n \in [1,N]
x_{n}     ~ NorMV(mu_{z_{n}}, si2)       , n \in [1,N]

If you are used to reading hierarchical models, it should be quite clear what this model does. Moreover, by keeping to a very LaTeX-like style, it is quite straightforward to automatically typeset any hierarchical model. If this file were stored in mix_gauss.hier, and if we had data for x stored in a file called X, we could run this model (with two Gaussians) directly by saying:
  hbc simulate --loadM X x N dim --define K 2 mix_gauss.hier

Perhaps closer to my heart would be a six-line implementation of the Latent Dirichlet Allocation model, complete with hyperparameter estimation:
  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}]

This code can either be run directly (eg., by a simulate call as above) or compiled to native C code for (much) faster execuation.