Gradient-based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds
This work addresses experimental design for complex scientific models where traditional methods fail, though it is incremental as it builds on existing lower bounds.
The authors tackled the problem of Bayesian experimental design for implicit models, where data distributions are intractable but samplable, by introducing a framework that maximizes mutual information lower bounds parameterized by neural networks, achieving results validated on toy models and a challenging epidemiology system.
We introduce a framework for Bayesian experimental design (BED) with implicit models, where the data-generating distribution is intractable but sampling from it is still possible. In order to find optimal experimental designs for such models, our approach maximises mutual information lower bounds that are parametrised by neural networks. By training a neural network on sampled data, we simultaneously update network parameters and designs using stochastic gradient-ascent. The framework enables experimental design with a variety of prominent lower bounds and can be applied to a wide range of scientific tasks, such as parameter estimation, model discrimination and improving future predictions. Using a set of intractable toy models, we provide a comprehensive empirical comparison of prominent lower bounds applied to the aforementioned tasks. We further validate our framework on a challenging system of stochastic differential equations from epidemiology.