GPdoemd: a Python package for design of experiments for model discrimination
| Autor: | Sebastian Niedenführ, Simon Olofsson, Lukas Hebing, Marc Peter Deisenroth, Ruth Misener |
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| Rok vydání: | 2018 |
| Předmět: |
FOS: Computer and information sciences
Computer science 020209 energy General Chemical Engineering Optimal design of experiments Model parameters Machine Learning (stat.ML) 02 engineering and technology Machine learning computer.software_genre symbols.namesake 020401 chemical engineering Statistics - Machine Learning 0202 electrical engineering electronic engineering information engineering 0204 chemical engineering Gaussian process computer.programming_language Mathematical model business.industry Design of experiments Experimental data Python (programming language) Computer Science Applications Approximate inference symbols Computer Science - Mathematical Software Artificial intelligence business computer Mathematical Software (cs.MS) |
| Zdroj: | Computers & Chemical Engineering |
| DOI: | 10.48550/arxiv.1810.02561 |
| Popis: | Model discrimination identifies a mathematical model that usefully explains and predicts a given system’s behaviour. Researchers will often have several models, i.e. hypotheses, about an underlying system mechanism, but insufficient experimental data to discriminate between the models, i.e. discard inaccurate models. Given rival mathematical models and an initial experimental data set, optimal design of experiments suggests maximally informative experimental observations that maximise a design criterion weighted by prediction uncertainty. The model uncertainty requires gradients, which may not be readily available for black-box models. This paper (i) proposes a new design criterion using the Jensen-Renyi divergence, and (ii) develops a novel method replacing black-box models with Gaussian process surrogates. Using the surrogates, we marginalise out the model parameters with approximate inference. Results show these contributions working well for both classical and new test instances. We also (iii) introduce and discuss GPdoemd, the open-source implementation of the Gaussian process surrogate method. |
| Databáze: | OpenAIRE |
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