Scalable method for Bayesian experimental design without integrating over posterior distribution

TL;DR

This paper introduces a scalable, likelihood-free Bayesian experimental design method that efficiently estimates the expected posterior variance without sampling the posterior, significantly reducing computational costs in PDE-based models.

Contribution

It presents a novel approach using the law of total variance and neural networks to approximate the expected conditional variance without posterior sampling, enhancing efficiency.

Findings

Reduces the number of observational model evaluations compared to importance sampling.

Provides an asymptotic error estimate for the proposed variance estimator.

Demonstrates effectiveness on PDE-based experimental design problems.

Abstract

We address the computational efficiency in solving the A-optimal Bayesian design of experiments problems for which the observational map is based on partial differential equations and, consequently, is computationally expensive to evaluate. A-optimality is a widely used and easy-to-interpret criterion for Bayesian experimental design. This criterion seeks the optimal experimental design by minimizing the expected conditional variance, which is also known as the expected posterior variance. This study presents a novel likelihood-free approach to the A-optimal experimental design that does not require sampling or integrating the Bayesian posterior distribution. The expected conditional variance is obtained via the variance of the conditional expectation using the law of total variance, and we take advantage of the orthogonal projection property to approximate the conditional expectation. We derive an asymptotic error estimation for the proposed estimator of the expected conditional variance and show that the intractability of the posterior distribution does not affect the performance of our approach. We use an artificial neural network (ANN) to approximate the nonlinear conditional expectation in the implementation of our method. We then extend our approach for dealing with the case that the domain of experimental design parameters is continuous by integrating the training process of the ANN into minimizing the expected conditional variance. Through numerical experiments, we demonstrate that our method greatly reduces the number of observation model evaluations compared with widely used importance sampling-based approaches. This reduction is crucial, considering the high computational cost of the observational models. Code is available at https://github.com/vinh-tr-hoang/DOEviaPACE.