Package Info


Common types for sampling


Common types for implementing Markov Chain Monte Carlo (MCMC) algorithms.

An instance of an MCMC problem can be characterized by the following:

  • A /target distribution/ over some parameter space

  • A /parameter space/ for a Markov chain to wander over

  • A /transition operator/ to drive the Markov chain

/mcmc-types/ provides the suitably-general 'Target', 'Chain', and 'Transition' types for representing these things respectively.

License: MIT



Package Version Update ID Released Package Hub Version Platforms Subpackages
1.0.3-bp150.2.3 info GA Release 2018-08-01 15
  • AArch64
  • ghc-mcmc-types
  • ghc-mcmc-types-devel
1.0.3-bp150.2.7 info GA Release 2018-07-31 15
  • ppc64le
  • ghc-mcmc-types
  • ghc-mcmc-types-devel
1.0.3-bp150.2.6 info GA Release 2018-07-30 15
  • x86-64
  • ghc-mcmc-types
  • ghc-mcmc-types-devel