Access modules for the MultiNest and Cuba integration libraries
This library provides programmatic access to MultiNest and PyCuba.
What is MultiNest?
MultiNest is a program and a sampling technique. As a Bayesian inference technique, it allows parameter estimation and model selection. (find out more in the MultiNest paper, http://arxiv.org/abs/0809.3437, or in a classic MCMC sampler, http://apemost.sf.net/ ). Recently, MultiNest added Importance Nested Sampling (INS, see http://arxiv.org/abs/1306.2144) which is now also supported.
The efficient Monte Carlo algorithm for sampling the parameter space is based on nested sampling and the idea of disjoint multi-dimensional ellipse sampling.
For the scientific community, where Python is becoming the new lingua franca (luckily), I provide an interface to MultiNest.
The automatic build makes sure both Python 2.7 and Python 3 are working correctly with MultiNest and Cuba. It also tests that MultiNest works with MPI enabled.
What does PyMultiNest do?
- provides an easy-to-use interface to MultiNest and Cuba integration algorithms
- allows connecting with your existing scientific Python code (numpy, scipy)
- allows Prior & LogLikelihood functions written in Python.
- Easy plotting, visualization and summary of MultiNest results.
- Running MultiNest with MPI
The plotting can also be run on existing MultiNest output, and when not using PyMultiNest for running MultiNest.
Questions and Problems
For any questions or problems with the software, please open an issue. This helps other people google the same question.
Using MultiNest with Python?
Look at the documentation available at http://johannesbuchner.github.com/PyMultiNest/index.html
What is PyCuba?
Cuba (http://www.feynarts.de/cuba/, https://github.com/JohannesBuchner/cuba) is a multidimensional numerical integration library for low dimensions. PyCuba allows integration of Python functions, providing an advanced alternative to the basic functions provided in scipy.integrate.
In the Bayesian sense, it is possible to use Cuba for model selection.
Q: Python callback functions are too slow!
If you really identified that your callback functions are too slow, even when using the usual tricks (numpy, etc.), you can implement and compile them as C functions.
You still have the neat python interface (default parameters, etc.), but achieve full execution speed, as only native code is executed while MultiNest runs.
Release history Release notifications
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|pymultinest-2.6-py2.7.egg (52.7 kB) Copy SHA256 hash SHA256||Egg||2.7||Nov 27, 2017|
|pymultinest-2.6-py2.py3-none-any.whl (27.6 kB) Copy SHA256 hash SHA256||Wheel||2.7||Nov 27, 2017|
|pymultinest-2.6.tar.gz (862.1 kB) Copy SHA256 hash SHA256||Source||None||Nov 27, 2017|