Samplers
========
### Here lies a library of combinators for MCMC kernels and proposals
- The relevant modules are `Kernels`, `Distributions`, and `Actions`
- See `Tests.hs` for some examples on how this library can be used
- Needs the [hmatrix](http://hackage.haskell.org/package/hmatrix) package
- Might need to do `cabal install hmatrix`
##### On Gibbs.hs
- The current implementation is for a Naive Bayes model
- TODO:
- Use an existing, "real" dataset instead of randomly generating sentences
- See which words appear most frequently for each label/class
- Average over all theta estimates and return top 10 and bottom 10 words
according to these averages
- Implement burn-in and lag (to decrease autocorrelation)