A new interesting open source tool from Merck,
The Bayesian Back End (BayBE) provides a general-purpose toolbox for Bayesian Design of Experiments, focusing on additions that enable real-world experimental campaigns. Besides functionality to perform a typical recommend-measure loop, BayBE’s highlights are:
- Custom parameter encodings: Improve your campaign with domain knowledge
- Built-in chemical encodings: Improve your campaign with chemical knowledge
- Single and multiple targets with min, max and match objectives
- Custom surrogate models: For specialized problems or active learning
- Hybrid (mixed continuous and discrete) spaces
- Transfer learning: Mix data from multiple campaigns and accelerate optimization
- Comprehensive backtest, simulation and imputation utilities: Benchmark and find your best settings
- Fully typed and hypothesis-tested: Robust code base
- All objects are fully de-/serializable: Useful for storing results in databases or use in wrappers like APIs