# A layered multiple importance sampling scheme for focused optimal Bayesian experimental design

arXiv: https://arxiv.org/abs/1903.11187 · HTML: https://mlsanity.com/paper/1903.11187/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2019.

## Built on by (most cited first, 9 of 9)

- [Learning non-Gaussian graphical models via Hessian scores and triangular transport](https://mlsanity.com/paper/2101.03093/) (2021)
- [Variational Bayesian Optimal Experimental Design with Normalizing Flows](https://mlsanity.com/paper/2404.13056/) (2024)
- [Bayesian model calibration for block copolymer self-assembly: Likelihood-free inference and expected information gain computation via measure transport](https://mlsanity.com/paper/2206.11343/) (2022)
- [Goal-Oriented Bayesian Optimal Experimental Design for Nonlinear Models using Markov Chain Monte Carlo](https://mlsanity.com/paper/2403.18072/) (2024)
- [Variational Sequential Optimal Experimental Design using Reinforcement Learning](https://mlsanity.com/paper/2306.10430/) (2023)
- [Expected information gain estimation via density approximations: Sample allocation and dimension reduction](https://mlsanity.com/paper/2411.08390/) (2024)
- [Coupled Input-Output Dimension Reduction: Application to Goal-oriented Bayesian Experimental Design and Global Sensitivity Analysis](https://mlsanity.com/paper/2406.13425/) (2024)
- [Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)](https://mlsanity.com/paper/2510.22517/) (2025)
- [Bayesian experimental design: grouped geometric pooled posterior via ensemble Kalman methods](https://mlsanity.com/paper/2604.18505/) (2026)

## Abstract (arXiv)

We develop a new computational approach for "focused" optimal Bayesian experimental design with nonlinear models, with the goal of maximizing expected information gain in targeted subsets of model parameters. Our approach considers uncertainty in the full set of model parameters, but employs a design objective that can exploit learning trade-offs among different parameter subsets. We introduce a new layered multiple importance sampling scheme that provides consistent estimates of expected information gain in this focused setting. This sampling scheme yields significant reductions in estimator bias and variance for a given computational effort, making optimal design more tractable for a wide range of computationally intensive problems.