Emcee version 2.0

Authors: Daniel Foreman-Mackey, David W. Hogg, Dustin Lang, Jonathan Goodman

Website: http://dan.iel.fm/emcee/current/#

Documentation: http://arxiv.org/abs/1202.3665

emcee is a python implementation of an affine invariant Markov chain Monte Carlo (MCMC) ensemble sampler (Goodman & Weare 2010). The emcee sampler simultaneously evolves an ensemble of walkers where the proposal distribution of one walker is updated based on the position of all other walkers in a complementary ensemble.

The output will be (walkers*samples) number of steps for each parameter.

Usage

cosmosis [ini]

mpirun cosmosis --mpi [ini]

See the page on MPI for information on using MPI to do parallel sampling.

Options reference

The following options can be used in the emcee section

walkers

Required integer number of walkers to use in ensemble. Minimum is 2*nparam+2, but more than that is usually better. For a 6D space we usually use a few hundred. Must be even to use MPI mode.

samples

Required integer number of steps each walker takes, the default = 1000. total samples = walkers*samples

nsteps

Required integer. Sample points are output every nsteps, default = 100 since a single emcee step evaluates once for each walker you should use a small number for this nsteps = 1

start_points

Optional string name of file containing contains at least (#walkers,#params) values to use as starting point for new chain. You can use this to continue an emcee run from where it left off - use the old chain as the starting point for the new one. If you do this then make sure to rename the file first, or change to a new output filename - otherwise it will be over-written.

random_start

Optional Boolean T/F. Ignored if start_file is set. Otherwise if set to True, randomly scatter the walkers throughout the flat prior. This is not usually a good idea unless you're really unsure about the starting point as the burn-in can be very slow. The (default) alternative is to start from a small ball around the starting position in the values file. If you do this then make sure the starting point is high likelihood - emcee has quite poor burn-in behaviour. You can use the maxlike sampler to find a good point.