Package license: MIT
Feedstock license: BSD 3-Clause
Summary: Python implementation of Bayesian Approximate Posterior Estimation algorithm
This package is a Python implementation of Bayesian Active Learning for Posterior Estimation by Kandasamy et al. (2015) and Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions by Wang & Li (2017). These algorithms allows the user to compute approximate posterior probability distributions using computationally expensive forward models by training a Gaussian Process (GP) surrogate for the likelihood evaluation. The algorithms leverage the inherent uncertainty in the GP's predictions to identify high-likelihood regions in parameter space where the GP is uncertain. The algorithms then run the forward model at these points to compute their likelihood and re-trains the GP to maximize the GP's predictive ability while minimizing the number of forward model evaluations. Check out Bayesian Active Learning for Posterior Estimation by Kandasamy et al. (2015) and Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functions by Wang & Li (2017) for in-depth descriptions of the respective algorithms.
Current build status
Current release info
approxposterior from the
conda-forge channel can be achieved by adding
conda-forge to your channels with:
conda config --add channels conda-forge
conda-forge channel has been enabled,
approxposterior can be installed with:
conda install approxposterior
It is possible to list all of the versions of
approxposterior available on your platform with:
conda search approxposterior --channel conda-forge
conda-forge is a community-led conda channel of installable packages. In order to provide high-quality builds, the process has been automated into the conda-forge GitHub organization. The conda-forge organization contains one repository for each of the installable packages. Such a repository is known as a feedstock.
A feedstock is made up of a conda recipe (the instructions on what and how to build the package) and the necessary configurations for automatic building using freely available continuous integration services. Thanks to the awesome service provided by CircleCI, AppVeyor and TravisCI it is possible to build and upload installable packages to the conda-forge Anaconda-Cloud channel for Linux, Windows and OSX respectively.
To manage the continuous integration and simplify feedstock maintenance
conda-smithy has been developed.
conda-forge.yml within this repository, it is possible to re-render all of
this feedstock's supporting files (e.g. the CI configuration files) with
conda smithy rerender.
For more information please check the conda-forge documentation.
feedstock - the conda recipe (raw material), supporting scripts and CI configuration.
conda-smithy - the tool which helps orchestrate the feedstock.
Its primary use is in the construction of the CI
and simplify the management of many feedstocks.
conda-forge - the place where the feedstock and smithy live and work to produce the finished article (built conda distributions)
If you would like to improve the approxposterior recipe or build a new
package version, please fork this repository and submit a PR. Upon submission,
your changes will be run on the appropriate platforms to give the reviewer an
opportunity to confirm that the changes result in a successful build. Once
merged, the recipe will be re-built and uploaded automatically to the
conda-forge channel, whereupon the built conda packages will be available for
everybody to install and use from the
Note that all branches in the conda-forge/approxposterior-feedstock are
immediately built and any created packages are uploaded, so PRs should be based
on branches in forks and branches in the main repository should only be used to
build distinct package versions.
In order to produce a uniquely identifiable distribution: