Markov chain Monte Carlo (MCMC) estimation provides a solution to the
complex integration problems that are faced in the Bayesian analysis
of statistical problems.  The implementation of MCMC algorithms is,
however, code intensive and time consuming.  We have developed a
Python package, which is called PyMCMC, that aids in the construction
of MCMC samplers and helps to substantially reduce the likelihood of
coding error, as well as aid in the minimisation of repetitive code.
PyMCMC contains classes for Gibbs, Metropolis Hastings, independent
Metropolis Hastings, random walk Metropolis Hastings, orientational
bias Monte Carlo and slice samplers as well as specific modules for
common models such as a module for Bayesian regression analysis.
PyMCMC is straightforward to optimise, taking advantage of the Python
libraries Numpy and Scipy, as well as being readily extensible with C
or Fortran.

