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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.


  1. Prerequisites
  2. 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

  1. 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
  1. 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 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 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:
In c shell:


‘modern-package-template’ -



Release date: 28-Sep-2015

  • This version includes a documentation and examples


Release date: 28-Sep-2015

  • Initial commit

Project details

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