Combinators for MCMC sampling

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BSD3 licensed by Praveen Narayanan
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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)
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