Bayesian experimental design: grouped geometric pooled posterior via ensemble Kalman methods

TL;DR

This paper introduces a grouped geometric pooled posterior framework using ensemble Kalman methods to enhance Bayesian experimental design accuracy while maintaining computational efficiency.

Contribution

It proposes a novel grouping strategy with ensemble Kalman inversion to improve posterior approximation in complex systems without extra model evaluations.

Findings

Improved posterior accuracy with grouped pooling approach.

Maintained computational cost comparable to amortized methods.

Validated on Gaussian-linear and high-dimensional models.

Abstract

Bayesian experimental design (BED) for complex physical systems is often limited by the nested inference required to estimate the expected information gain (EIG) or its gradients. Each outer sample induces a different posterior, creating a large and heterogeneous set of inference targets. Existing methods have to sacrifice either accuracy or efficiency: they either perform per-outer-sample posterior inference, which yields higher fidelity but at prohibitive computational cost, or amortize the inner inference across all outer samples for computational reuse, at the risk of degraded accuracy under posterior heterogeneity. To improve accuracy and maintain cost at the amortized level, we propose a grouped geometric pooled posterior framework that partitions outer samples into groups and constructs a pooled proposal for each group. While such grouping strategy would normally require generating separate proposal samples for different groups, our tailored ensemble Kalman inversion (EKI) formulation generates these samples without extra forward-model evaluation cost. We also introduce a conservative diagnostic to assess importance-sampling quality to guide grouping. This grouping strategy improves within-group proposal-target alignment, yielding more accurate and stable estimators while keeping the cost comparable to amortized approaches. We evaluate the performance of our method on both Gaussian-linear and high-dimensional network-based model discrepancy calibration problems.