Bayesian Optimization for Adaptive Experimental Design: A Review

• Main Authors: Stewart Greenhill, Santu Rana, Sunil Gupta, Pratibha Vellanki, Svetha Venkatesh
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Bayesian methods; design for experiments; design optimization; machine learning algorithms
• Online Access: https://ieeexplore.ieee.org/document/8957442/
• ISSN: 2169-3536

Bayesian optimisation is a statistical method that efficiently models and optimises expensive “black-box” functions. This review considers the application of Bayesian optimisation to experimental design, in comparison to existing Design of Experiments (DOE) methods. Solutions are surveyed for a range of core issues in experimental design including: the incorporation of prior knowledge, high dimensional optimisation, constraints, batch evaluation, multiple objectives, multi-fidelity data, and mixed variable types.