emdgroup/baybe
A Python library for Bayesian optimization and sequential experiment design using probabilistic surrogate models.
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BayBE implements Bayesian optimization methods for efficiently finding optimal configurations in high-dimensional parameter spaces. It provides surrogate model-based strategies, acquisition functions, and experimental design primitives that iteratively select promising evaluation candidates. The library is designed for scenarios like hyperparameter tuning, materials discovery, and process optimization.
Frequently asked
- What is emdgroup/baybe?
- A Python library for Bayesian optimization and sequential experiment design using probabilistic surrogate models.
- Is baybe open source?
- Yes — emdgroup/baybe is open source, released under the Apache-2.0 license.
- What language is baybe written in?
- emdgroup/baybe is primarily written in Python.
- How popular is baybe?
- emdgroup/baybe has 488 stars on GitHub.
- Where can I find baybe?
- emdgroup/baybe is on GitHub at https://github.com/emdgroup/baybe.