# Approximate Bayesian Computation with Domain Expert in the Loop

arXiv: https://arxiv.org/abs/2201.12090 · HTML: https://mlsanity.com/paper/2201.12090/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 52 references that are in the map.
- First posted on arXiv 28 Jan 2022.

## Builds on (most cited first, 13 of 13)

- [Flexible statistical inference for mechanistic models of neural dynamics](https://mlsanity.com/paper/1711.01861/) (2017)
- [On Nesting Monte Carlo Estimators](https://mlsanity.com/paper/1709.06181/) (2017)
- [Neural Approximate Sufficient Statistics for Implicit Models](https://mlsanity.com/paper/2010.10079/) (2020)
- [Likelihood-free inference with emulator networks](https://mlsanity.com/paper/1805.09294/) (2018)
- [Fast ε-free Inference of Simulation Models with Bayesian Conditional Density Estimation](https://mlsanity.com/paper/1605.06376/) (2016)
- [Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network](https://mlsanity.com/paper/1510.02175/) (2015)
- [A General Method for Calibrating Stochastic Radio Channel Models with Kernels](https://mlsanity.com/paper/2012.09612/) (2020)
- [Improving drug sensitivity predictions in precision medicine through active expert knowledge elicitation](https://mlsanity.com/paper/1705.03290/) (2017)
- [ABC-CDE: Towards Approximate Bayesian Computation with Complex High-Dimensional Data and Limited Simulations](https://mlsanity.com/paper/1805.05480/) (2018)
- [Knowledge Elicitation via Sequential Probabilistic Inference for High-Dimensional Prediction](https://mlsanity.com/paper/1612.03328/) (2016)
- [User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction](https://mlsanity.com/paper/1710.04881/) (2017)
- [Summary Statistics in Approximate Bayesian Computation](https://mlsanity.com/paper/1512.05633/) (2015)
- [Regression approaches for Approximate Bayesian Computation](https://mlsanity.com/paper/1707.01254/) (2017)

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

- [Learning Robust Statistics for Simulation-based Inference under Model Misspecification](https://mlsanity.com/paper/2305.15871/) (2023)
- [Leveraging Self-Consistency for Data-Efficient Amortized Bayesian Inference](https://mlsanity.com/paper/2310.04395/) (2023)
- [Optimally-Weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference](https://mlsanity.com/paper/2301.11674/) (2023)
- [Simulation-based Bayesian inference under model misspecification](https://mlsanity.com/paper/2503.12315/) (2025)
- [Cost-aware simulation-based inference](https://mlsanity.com/paper/2410.07930/) (2024)
- [Robust Simulation-Based Inference under Missing Data via Neural Processes](https://mlsanity.com/paper/2503.01287/) (2025)
- [Multilevel neural simulation-based inference](https://mlsanity.com/paper/2506.06087/) (2025)
- [Flow Matching Calibration for Simulation-Based Inference under Model Misspecification](https://mlsanity.com/paper/2509.23385/) (2025)
- [Misspecification-robust amortised simulation-based inference using variational methods](https://mlsanity.com/paper/2509.05724/) (2025)

## Abstract (arXiv)

Approximate Bayesian computation (ABC) is a popular likelihood-free inference method for models with intractable likelihood functions. As ABC methods usually rely on comparing summary statistics of observed and simulated data, the choice of the statistics is crucial. This choice involves a trade-off between loss of information and dimensionality reduction, and is often determined based on domain knowledge. However, handcrafting and selecting suitable statistics is a laborious task involving multiple trial-and-error steps. In this work, we introduce an active learning method for ABC statistics selection which reduces the domain expert's work considerably. By involving the experts, we are able to handle misspecified models, unlike the existing dimension reduction methods. Moreover, empirical results show better posterior estimates than with existing methods, when the simulation budget is limited.