@hackage mcmc-types1.0.3

Common types for sampling.

Categories
- Mathematics
License
MIT
Maintainer
jared@jtobin.ca
Links
Versions
- 1.0.3 Sun, 4 Dec 2016
- 1.0.2 Mon, 31 Oct 2016
- 1.0.1 Tue, 6 Oct 2015
- 1.0.0 Tue, 6 Oct 2015

Installation
In your cabal file:
Dependencies (4)
- base >=4 && <6
- containers >=0.5 && <6
- transformers >=0.5 && <1.0
- mwc-probability >=1.0.1
Dependents (5)
@hackage/declarative, @hackage/flat-mcmc, @hackage/hasty-hamiltonian, @hackage/speedy-slice, @hackage/mighty-metropolis

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.