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
Metadata
Release files for hoppMCMC 2.2.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| hoppmcmc-2.2.7.tar.gz | 600.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| hoppmcmc-2.2.7-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.2 MB
Release files / hoppmcmc-2.2.7.tar.gz
| Download URL | hoppmcmc-2.2.7.tar.gz |
|---|---|
| Size | 600.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.2.0 CPython/3.12.0
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Release files / hoppmcmc-2.2.7-py3-none-any.whl
| Download URL | hoppmcmc-2.2.7-py3-none-any.whl |
|---|---|
| Size | 600.3 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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BLAKE2b-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/6.2.0 CPython/3.12.0
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