Skip to main content
An adaptive basin-hopping Markov-chain Monte Carlo algorithm for Bayesian optimisation
======================================================================================

This is the python (v3.7) 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
--------

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

2) 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

Download files

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

Source Distribution

hoppmcmc-2.2.0.tar.gz (600.1 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

hoppmcmc-2.2.0-py3-none-any.whl (600.2 kB view details)

Uploaded Python 3

File details

Details for the file hoppmcmc-2.2.0.tar.gz.

File metadata

  • Download URL: hoppmcmc-2.2.0.tar.gz
  • Upload date:
  • Size: 600.1 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.0

File hashes

Hashes for hoppmcmc-2.2.0.tar.gz
Algorithm Hash digest
SHA256 301d09165d9490b4b40791c5eb8307b4d93a0f2d4cae948869482160e60dc5c4
MD5 bbd57c9da97d58202cde9ca62ccab30b
BLAKE2b-256 2eecebf6eaba218a40e4318fc160201948fb7fe471b9e4f8a9a04d83f791d5f0

See more details on using hashes here.

File details

Details for the file hoppmcmc-2.2.0-py3-none-any.whl.

File metadata

  • Download URL: hoppmcmc-2.2.0-py3-none-any.whl
  • Upload date:
  • Size: 600.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.12.0

File hashes

Hashes for hoppmcmc-2.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 caab10b4aaae0694ab4e621edd7993420070fb21c52b0ba81eeaa760ea6a6fb9
MD5 5893503af036d628942e6d894c1e2a05
BLAKE2b-256 78a43d386097675d200b03c7e5b33fb74a777e17d68826582fd47161a9a380bd

See more details on using hashes here.

Release history Release notifications | RSS feed

2.2.7

2 files

2.2.6

2 files

2.2.5

2 files

2.2.4

2 files

2.2.3

2 files

2.2.2

2 files

2.2.1

2 files

This release

2.2.0 This release

2 files

2.1.0

2 files

2.0.0

2 files

1.1

1 file

1.0

1 file

0.5

1 file

0.4

1 file

0.3

1 file

0.2

1 file

0.1

1 file

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page