MontePython 3: boosted MCMC sampler and other features
  MontePython is a parameter inference package for cosmology. We present the
latest development of the code over the past couple of years. We explain, in
particular, two new ingredients both contributing to improve the performance of
Metropolis-Hastings sampling: an adaptation algorithm for the jumping factor,
and a calculation of the inverse Fisher matrix, which can be used as a proposal
density. We present several examples to show that these features speed up
convergence and can save many hundreds of CPU-hours in the case of difficult
runs, with a poor prior knowledge of the covariance matrix. We also summarise
all the functionalities of MontePython in the current release, including new
likelihoods and plotting options.
