Add a built-in mcmc sampler

This adds a built-in MCMC sampler. Predominantly, the ideas used are built on the ideas of Veitch et al. (2015). This started off as an exercise in understanding the LALInference_MCMC implementation.

Motivation

• It would be nice to have an MCMC sampler which we can easily customize/modify
• I decided it was easier to implement from scratch than use existing packages

Current feature set

• Custom jump proposals
• Parallel-tempering (tested to resolve sampling issues for a simple bimodal problem)
• Parallelization using a map

Required features

• A user interface for selecting/weighting custom jump proposals
• More proposal options (including GW-specific versions)
• Thermodynamic integration to estimate the evidence
• Checkpointing
• Between chain swap-acceptance information
• Profile and optimize the sample storage: currently used pandas dataframes, but I believe this is adding significant overhead.

Nice features I'd like to add

• Improve parallelization: currently the efficiency is awful, I think it might be because I'm passing data around
• Samples from the finite-temperature chains (use resampling to reweight from the finite-temperature distribution to the zero-temperature distribution)
• Specify initial positions per-chain
• Specify the temperature ladder
• Implement dynamic temperature selection: arXiv:1501.05823