bayesian-experimental-design

ALINE: Joint Amortization for Bayesian Inference and Active Data Acquisition (Huang et al., NeurIPS 2025)

Knowledge elicitation when the user can give feedback to different features of the model with the goal to improve the prediction on the test data in a "smal n, large p" setting.

Amortized Bayesian Experimental Design for Decision-Making (Huang et al., NeurIPS 2024)

We investigate several reinforcement learning algorithms on three Bayesian experimental design problems. Performance is measured by each agent's training time and generalisability to various experimental setups at evaluation time.

Source code for Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation, ICML 2020, https://arxiv.org/abs/2002.08129

Belief-state deep reinforcement learning for information-efficient autonomous beach microplastic sampling

kinn — a Bayesian diagnostic interview engine. Built with Opus 4.7 hackathon submission, Apr 21–28 2026.

Code for the paper "Gradient-Based Bayesian Experimental Design for Implicit Models using Mutual Information Lower Bounds" https://arxiv.org/abs/2105.04379

Python code for "Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods", NeurIPS, 2021, https://proceedings.neurips.cc/paper/2021/hash/d811406316b669ad3d370d78b51b1d2e-Abstract.html

Code for paper "Bayesian I-optimal designs for choice experiments with mixtures" by Mario Becerra and Peter Goos.

Implementation of Bayesian experimental design using regularized determinantal point processes

Code and frozen artifacts for replicated-bank dual-objective repair in learned sequential experimental design

objective based BOED on power grid application

Source code for "Efficient Bayesian Experimental Design for Implicit Models", AISTATS 2019, https://arxiv.org/abs/1810.09912

Code for the paper "Sequential Bayesian Experimental Design for Implicit Models via Mutual Information", Bayesian Analysis 2021, https://arxiv.org/abs/2003.09379.

Auditable Bayesian calibration and expected information gain (EIG) experimental design for magnetic components.

In this work, we develop a new framework for designing experiments that are robust to model misspecification through generalised Bayesian inference. This repository contains the files needed to perform Generalised Bayesian Optimal Experimental Design (GBOED) on several experimental design problems.

Autonomy-Dissolver Reasoning Engine: causal discovery that converts apparent autonomy into mechanism — with CI-verified, seed-exact reproduction of every published number.