Multimodal information gain in Bayesian design of experiments

Quan Long ()

Computational Statistics, 2022, vol. 37, issue 2, No 14, 865-885

Abstract: Abstract One of the well-known challenges in optimal experimental design is how to efficiently estimate the nested integrations of the expected information gain. The Gaussian approximation and associated importance sampling have been shown to be effective at reducing the numerical costs. However, they may fail due to the non-negligible biases and the numerical instabilities. A new approach is developed to compute the expected information gain, when the posterior distribution is multimodal—a situation previously ignored by the methods aiming at accelerating the nested numerical integrations. Specifically, the posterior distribution is approximated using a mixture distribution constructed by multiple runs of global search for the modes and weighted local Laplace approximations. Under any given probability of capturing all the modes, we provide an estimation of the number of runs of searches, which is dimension independent. It is shown that the novel global-local multimodal approach can be significantly more accurate and more efficient than the other existing approaches, especially when the number of modes is large. The methods can be applied to the designs of experiments with both calibrated and uncalibrated observation noises.

Keywords: Weighted Laplace approximation; Gaussian mixture; Expected information gain; Optimal design of experiments; Machine learning (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s00180-021-01145-9

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