# Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches

arXiv: https://arxiv.org/abs/1802.04170 · HTML: https://mlsanity.com/paper/1802.04170/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Feb 2018.

## Built on by (most cited first, 3 of 3)

- [Toward Operationalizing Pipeline-aware ML Fairness: A Research Agenda for Developing Practical Guidelines and Tools](https://mlsanity.com/paper/2309.17337/) (2023)
- [Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty](https://mlsanity.com/paper/2102.03782/) (2021)
- [What Can a Gaussian Process Design Test](https://mlsanity.com/paper/2610.10122/) (2026)

## Abstract (arXiv)

Healthcare companies must submit pharmaceutical drugs or medical devices to regulatory bodies before marketing new technology. Regulatory bodies frequently require transparent and interpretable computational modelling to justify a new healthcare technology, but researchers may have several competing models for a biological system and too little data to discriminate between the models. In design of experiments for model discrimination, the goal is to design maximally informative physical experiments in order to discriminate between rival predictive models. Prior work has focused either on analytical approaches, which cannot manage all functions, or on data-driven approaches, which may have computational difficulties or lack interpretable marginal predictive distributions. We develop a methodology introducing Gaussian process surrogates in lieu of the original mechanistic models. We thereby extend existing design and model discrimination methods developed for analytical models to cases of non-analytical models in a computationally efficient manner.