The Bayesian Back End (BayBE) is 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:

• 🎯 Single and multiple targets with min, max and match objectives

• ⚙️ Custom surrogate models: For specialized problems or active learning

• 🚀 Transfer learning: Mix data from multiple campaigns and accelerate optimization

• 📈 Comprehensive backtest, simulation and imputation utilities: Benchmark and find your best settings

• 🔄 All objects are fully de-/serializable: Useful for storing results in databases or use in wrappers like APIs

BoFire is a Bayesian Optimization Framework Intended for Real Experiments.

• supports mixed continuous, discrete and categorical parameter spaces for system inputs and outputs,

• separates objectives (minimize, maximize, close-to-target) from the outputs on which they operate,

• supports different specific and generic constraints as well as black-box output constraints,

• can provide flexible DoEs that fulfill constraints,

• provides sampling methods for constrained mixed variable spaces,

• serializes problems for use in RESTful APIs and json/bson DBs,

• allows easy out of the box usage of strategies for single and multi-objective Bayesian optimization, and

• provides a high flexibility on the modelling side if needed.

ProcessOptimizer is a fork of scikit-optimize. ProcessOptimizer will fundamentally function like scikit-optimize, yet developments are focussed on bringing improvements to help optimizing real world processes, like chemistry or baking.

This package is intended for real world process optimization problems of black-box functions. This could e.g. be some complex chemical reaction where no reliable analytical model mapping input variables to the output is readily available.

Bayesian optimization is a great tool for optimizing black-box functions where the input space has many dimensions and the function is expensive to evaluate in terms of time and/or resources.

BoTorch is a library for Bayesian Optimization built on PyTorch.

• Provides a modular and easily extensible interface for composing Bayesian optimization primitives, including probabilistic models, acquisition functions, and optimizers.

• Harnesses the power of PyTorch, including auto-differentiation, native support for highly parallelized modern hardware (e.g. GPUs) using device-agnostic code, and a dynamic computation graph.

• Supports Monte Carlo-based acquisition functions via the reparameterization trick, which makes it straightforward to implement new ideas without having to impose restrictive assumptions about the underlying model.

• Enables seamless integration with deep and/or convolutional architectures in PyTorch.

• Has first-class support for state-of-the art probabilistic models in GPyTorch, including support for multi-task Gaussian Processes (GPs) deep kernel learning, deep GPs, and approximate inference.