First full-version release of PyVBMC, a Python package for efficient Bayesian inference. Full documentation is available at https://acerbilab.github.io/pyvbmc/. Feedback is welcome, see [troubleshooting and contact](https://github.com/acerbilab/pyvbmc#troubleshooting-and-contact). The same packaged version is also available at https://pypi.org/project/PyVBMC/#history.
Additional details of the algorithm can be found in the two Variational Bayesian Monte Carlo papers published at NeurIPS in [2018](https://papers.nips.cc/paper/8043-variational-bayesian-monte-carlo) and [2020](https://papers.nips.cc/paper/2020/hash/5d40954183d62a82257835477ccad3d2-Abstract.html).
A MATLAB implementation is also available at the [acerbilab/VBMC](https://github.com/acerbilab/vbmc) repository.
What's Changed
* feat: PyVBMC now accepts optional prior distributions. See [example 5](https://acerbilab.github.io/pyvbmc/_examples/pyvbmc_example_5_prior_distributions.html) for more details.
* fix: eliminate log warning by setting KL divergences to inf. by pipme in https://github.com/acerbilab/pyvbmc/pull/125
* Other minor/cosmetic changes.
**Full Changelog**: https://github.com/acerbilab/pyvbmc/compare/v0.9.1...v1.0.0