A Review of Modern Computational Algorithms for Bayesian Optimal Design

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Efficient Acquisition Rules for Model-Based Approximate Bayesian Computation (2017, methodology): “We note that the rationale of our approach is essentially the same as in probabilistic numerics literature (see e.g. Hennig et al. (2015)) or in sequential Bayesian experimental design (see Ryan et al. (2016) for a recent survey).”
Stratification Trees for Adaptive Randomization in Randomized Controlled Trials (2018, background): “For example, Simester et al. (2006), Karlan and Zinman (2008), and Karlan and Wood (2017) all feature a multi-stage structure, and Karlan and Appel (2016) advocate the use of pilot experiments to help avoid potential implementation failures when scaling up to the main study.”
On Variational Bounds of Mutual Information (2019, methodology): “Mutual information estimators are used in computational neuroscience (Palmer et al., 2015), Bayesian optimal experimental design (Ryan et al., 2016; Foster et al., 2018), understanding neural networks (Tishby et al., 2000; Tishby & Zaslavsky, 2015; Gabri´e et al., 2018), and more.”