Adaptive Gaussian process surrogates for Bayesian inference

2018/09/27 by Timur Takhtaganov, Juliane Müller, Takhtaganov, Timur +1
Computer Science · Decision Sciences · #60G15 #62F15 #62G08 #62K20 #62K86 #Advanced Multi-Objective Optimization Algorithms #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optimal Experimental Design Methods

paper · pdf · doi:10.48550/arxiv.1809.10784

openalex publication_date 2018/09/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

We present an adaptive approach to the construction of Gaussian process surrogates for Bayesian inference with expensive-to-evaluate forward models. Our method relies on the fully Bayesian approach to training Gaussian process models and utilizes the expected improvement idea from Bayesian global optimization. We adaptively construct training designs by maximizing the expected improvement in fit of the Gaussian process model to the noisy observational data. Numerical experiments on model problems with synthetic data demonstrate the effectiveness of the obtained adaptive designs compared to the fixed non-adaptive designs in terms of accurate posterior estimation at a fraction of the cost of inference with forward models.

Citations

• Predicting the output from a complex computer code when fast approximations are available
• Practical Bayesian Optimization of Machine Learning Algorithms

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