# Bayesian experimental design without posterior calculations: an adversarial approach

## TL;DR

This paper introduces an adversarial method for Bayesian experimental design that avoids costly posterior calculations by optimizing Fisher information, addressing invariance issues and improving design efficiency.

## Contribution

It proposes a novel adversarial approach that enhances Bayesian experimental design by eliminating the need for posterior computations and ensuring invariance to reparameterisation.

## Key findings

- The adversarial approach avoids posterior calculations.
- It maintains invariance to reparameterisation.
- It enables efficient gradient-based optimization.

## Abstract

Most computational approaches to Bayesian experimental design require making posterior calculations repeatedly for a large number of potential designs and/or simulated datasets. This can be expensive and prohibit scaling up these methods to models with many parameters, or designs with many unknowns to select. We introduce an efficient alternative approach without posterior calculations, based on optimising the expected trace of the Fisher information, as discussed by Walker (2016). We illustrate drawbacks of this approach, including lack of invariance to reparameterisation and encouraging designs in which one parameter combination is inferred accurately but not any others. We show these can be avoided by using an adversarial approach: the experimenter must select their design while a critic attempts to select the least favourable parameterisation. We present theoretical properties of this approach and show it can be used with gradient based optimisation methods to find designs efficiently in practice.