Skip to main content

Implementation of a Python MCMC gibbs-sampler with adaptive stepping

Project description

# GAStimator Implementation of a Python MCMC gibbs-sampler with adaptive stepping.

While this is a simple MCMC algorithm, it is robust and stable and well suited to high dimensional problems with many degrees of freedom and very sharp likelihood features. For instance kinematic modelling of datacubes with this code has been found to be orders of magnitude quicker with GAStimator than using more advanced affine-invariant MCMC methods.

### Install You can install GAStimator with pip install gastimator. Alternatively you can download the code here, navigate to the directory you unpack it too, and run python install.

It requires the following modules:

  • numpy
  • matplotlib
  • progress
  • plotbin

### Documentation

To get you started, see the walk through here:

Author & License

Copyright 2019 Timothy A. Davis

Built by Timothy A. Davis <>. Licensed under the GNU General Public License v3 (GPLv3) license (see LICENSE).

Project details

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for gastimator, version 0.2.0
Filename, size File type Python version Upload date Hashes
Filename, size gastimator-0.2.0.tar.gz (11.2 kB) File type Source Python version None Upload date Hashes View hashes

Supported by

Elastic Elastic Search Pingdom Pingdom Monitoring Google Google BigQuery Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page