Hackage :: [Package]

mcmc-samplers: A library of combinators to build MCMC kernels, proposals, and targets

[ bsd3, library, machine-learning, math, numeric, statistics ] [ Propose Tags ]

Downloads

Maintainer's Corner

Package maintainers

For package maintainers and hackage trustees

Candidates

  • No Candidates
Versions [RSS] 0.1.0.0, 0.1.1.0, 0.1.1.1
Dependencies base (>=4.6 && <4.7), containers (>=0.5 && <0.6), hmatrix (>=0.15 && <0.16), mwc-random (>=0.13 && <0.14), primitive (>=0.5 && <0.6), statistics (>=0.11 && <0.12), vector (>=0.10 && <0.11) [details]
License LicenseRef-PublicDomain
Author Praveen Narayanan
Maintainer revenap@gmail.com
Category Math
Uploaded by pravnar at 2014-06-21T10:21:47Z
Distributions
Reverse Dependencies 1 direct, 0 indirect [details]
Downloads 2276 total (11 in the last 30 days)
Rating 2.0 (votes: 1) [estimated by Bayesian average]
Your Rating
  • λ
  • λ
  • λ
Status Docs available [build log]
Successful builds reported [all 1 reports]

Readme for mcmc-samplers-0.1.0.0

[back to package description]

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)