MCEvidence

A python package implementing the MARGINAL LIKELIHOODS FROM MONTE CARLO MARKOV CHAINS algorithm described in Heavens et. al. (2017)

This code is tested in Python 2 version 2.7.12 and Python 3 version 3.5.2.

Notes

The MCEvidence algorithm is implemented using scikit nearest neighbour code.

Installation

To install this project into your machine using pip, do the following

 $ git clone https://github.com/yabebalFantaye/MCEvidence
 $ cd MCEvidence
 $ pip install . --editable

The "--editable" or "-e" extension in the last command is to install the project in the editable mode.

To install this project with pip without clonning

 $ pip install git+https://github.com/yabebalFantaye/MCEvidence

Examples

To run the evidence estimation from an ipython terminal or notebook

>> from MCEvidence import MCEvidence
>> MLE = MCEvidence('/path/to/chain').evidence()

You can find a more advanced example that uses MCEvidence to analyse a set of MCMC chains in planck_mcevidence.py. The result of our companion paper No evidence for extensions to the standard cosmological model is obtained using this code.

To run MCEvidence from shell

$ python MCEvidence.py </path/to/chain> [optional arguments]

You can check the allowed parameters by doing $ python MCEvidence.py -h

The output is:

usage: MCEvidence.py [-h] [-k KMAX] [-ic IDCHAIN] [-np NDIM] [-b BURNFRAC]
                     [-t THINFRAC] [-v VERBOSE] [--cosmo]
		                      root_name

Planck Chains MCEvidence. Returns the log Bayesian Evidence computed using the
kth NN.

 positional arguments:
   root_name             Root filename for MCMC chains or python class filename

 optional arguments:
   -h, --help            show this help message and exit
   -k KMAX, --kmax KMAX  scikit maximum K-NN
   -ic IDCHAIN, --idchain IDCHAIN
                    Which chains to use - the id e.g 1 means read only
                    *_1.txt (default=None - use all available)
   -np NDIM, --ndim NDIM
                    How many parameters to use (default=None - use all
                    params)
   -b BURNFRAC, --burnfrac BURNFRAC, --burnin BURNFRAC, --remove BURNFRAC
                    Burn-in fraction
   -t THINFRAC, --thin THINFRAC, --thinfrac THINFRAC
                    Thinning fraction
   -vb VERBOSE, --verbose VERBOSE
                    Verbosity of the code while running: The mapping between verbose number
                    and the logging module levels are: 0: WARNNING, 1: INFO, 2: DEBUG
                    setting verbose>2 outputs EVERYTHING
   --cosmo              
          	        Flag to compute prior_volume using cosmological
                    parameters only

If you use the code, please cite the following paper

.. [1] Heavens et. al. (2017)