An adaptive basin-hopping Markov-chain Monte Carlo algorithm for Bayesian optimisation
Project description
An adaptive basin-hopping Markov-chain Monte Carlo algorithm for Bayesian optimisation
This is the python (v2.7) implementation of the hoppMCMC algorithm aiming to identify and sample from the high-probability regions of a posterior distribution. The algorithm combines three strategies: (i) parallel MCMC, (ii) adaptive Gibbs sampling and (iii) simulated annealing. Overall, hoppMCMC resembles the basin-hopping algorithm implemented in the optimize module of scipy, but it is developed for a wide range of modelling approaches including stochastic models with or without time-delay.
Contents
Prerequisites
Linux installation
1) Prerequisites
The hoppMCMC algorithm requires the following packages, which are not included in this package:
numpy scipy mpi4py (MPI parallelisation)
The mpi4py package is required for parallelisation; however, it can be omitted.
2) Linux installation
Easy way:
- If you have pip installed, you can use the following command to download and install the package.
pip install hoppMCMC
- Alternatively, you can download the source code from PyPI and run pip on the latest version xxx.
pip install hoppMCMC-xxx.tar.gz
Hard way:
- If pip is not available, you can unpack the package contents and perform a manual install.
tar -xvzf hoppMCMC-xxx.tar.gz cd hoppMCMC-xxx python setup.py install
This will install the package in the site-packages directory of your python distribution. If you do not have root privileges or you wish to install to a different directory, you can use the –prefix argument.
python setup.py install –prefix=<dir>
In this case, please make sure that <dir> is in your PYTHONPATH, or you can add it with the following command.
- In bash shell:
export PYTHONPATH=<dir>:$PYTHONPATH
- In c shell:
setenv PYTHONPATH <dir>:$PYTHONPATH
Credits
‘modern-package-template’ - http://pypi.python.org/pypi/modern-package-template
News
0.2
Release date: 28-Sep-2015
This version includes a documentation and examples
0.1
Release date: 28-Sep-2015
Initial commit
Project details
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