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 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)