[tools] add plot_responsibility() to visualize clustering output
[make] fix `clean`
[GaussianInference] improve default initialization of ``m``

o "first": set initial component means to first data points
o "random": set initial component means to random data points
[examples] correct comments on VB parameters

o most important to manually set is "W0"
o "W0" is the inverse of the prioe covariance; it should be set
  such that "inv(W0)" is much smaller than the expected covariances.
  Otherwise, the resulting covariances are dominated by "W0" which is
  unwanted in the examples presented.
o "m" has a better default initialization now and is therefore not too
  critical any more.
[density] copy covariance exactly once; make sure data type is ``double``
[importance_sampling] add function ``combine_weights``

o ``combine_weights`` calculates the `deterministic mixture weights`
  given importance weighted samples and their proposals
[r_value] allow passing just variance instead of full covariance matrix

o functions ``make_r_gaussmix`` and ``make_r_tmix`` do no more calculate
  the full chain covariances but only the variance (covariance diagonals)
  using numpy.var
[mix_adapt/variational] Explain how to get the responsibility matrix
[make] run all test with mpi for check-sdist
[tools] add test for plot_responsibility
[pypmc] bump version to 0.9.1
[doc] add plot_responsibility
[doc] section "How to use this documentation": pypmc --> ``pypmc``
[tools] add parallel_function
[examples] add "ultimately_parallel.py"

o parallel sampling from a parallel likelihood function