Pybads

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0.8.2

Initial public beta version of PyBADS, a Python package for solving difficult and moderately expensive optimization problems. Full documentation is available at [acerbilab.github.io/pybads/](https://acerbilab.github.io/pybads/). Feedback is welcome, see [troubleshooting and contact](https://github.com/acerbilab/pybads#troubleshooting-and-contact). The same packaged version is also available at [pypi](https://pypi.org/project/PyBADS/#history).
Additional details of the algorithm can be found in the original Bayesian Adaptive Direct Search paper presented at [NeurIPS in 2017](https://papers.nips.cc/paper/2017/hash/df0aab058ce179e4f7ab135ed4e641a9-Abstract.html).
A MATLAB implementation is also available at the [acerbilab/BADS](https://github.com/acerbilab/bads) repository.

0.8.1

Initial public beta version of PyBADS, a Python package for solving difficult and moderately expensive optimization problems. Full documentation is available at [acerbilab.github.io/pybads/](https://acerbilab.github.io/pybads/). Feedback is welcome, see [troubleshooting and contact](https://github.com/acerbilab/pybads#troubleshooting-and-contact). The same packaged version is also available at [pypi](https://pypi.org/project/PyBADS/#history).
Additional details of the algorithm can be found in the original Bayesian Adaptive Direct Search paper presented at [NeurIPS in 2017](https://papers.nips.cc/paper/2017/hash/df0aab058ce179e4f7ab135ed4e641a9-Abstract.html).
A MATLAB implementation is also available at the [acerbilab/BADS](https://github.com/acerbilab/bads) repository.

0.8.0

Initial public beta version of PyBADS, a Python package for solving difficult and moderately expensive optimization problems. Full documentation is available at [acerbilab.github.io/pybads/](https://acerbilab.github.io/pybads/). Feedback is welcome, see [troubleshooting and contact](https://github.com/acerbilab/pybads#troubleshooting-and-contact). The same packaged version is also available at [pypi](https://pypi.org/project/PyBADS/#history).
Additional details of the algorithm can be found in the original Bayesian Adaptive Direct Search paper presented at [NeurIPS in 2017](https://papers.nips.cc/paper/2017/hash/df0aab058ce179e4f7ab135ed4e641a9-Abstract.html).
A MATLAB implementation is also available at the [acerbilab/BADS](https://github.com/acerbilab/bads) repository.

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