# Efficient acquisition rules for model-based approximate Bayesian computation

arXiv: https://arxiv.org/abs/1704.00520 · HTML: https://mlsanity.com/paper/1704.00520/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2017.

## Built on by (most cited first, 16 of 16)

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- [Approximate Bayesian inference from noisy likelihoods with Gaussian process emulated MCMC](https://mlsanity.com/paper/2104.03942/) (2021)
- [Active Nearest Neighbor Regression Through Delaunay Refinement](https://mlsanity.com/paper/2206.08061/) (2022)
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- [Deterministic Langevin Unconstrained Optimization with Normalizing Flows](https://mlsanity.com/paper/2310.00745/) (2023)
- [An efficient likelihood-free Bayesian inference method based on sequential neural posterior estimation](https://mlsanity.com/paper/2311.12530/) (2023)

## Abstract (arXiv)

Approximate Bayesian computation (ABC) is a method for Bayesian inference when the likelihood is unavailable but simulating from the model is possible. However, many ABC algorithms require a large number of simulations, which can be costly. To reduce the computational cost, Bayesian optimisation (BO) and surrogate models such as Gaussian processes have been proposed. Bayesian optimisation enables one to intelligently decide where to evaluate the model next but common BO strategies are not designed for the goal of estimating the posterior distribution. Our paper addresses this gap in the literature. We propose to compute the uncertainty in the ABC posterior density, which is due to a lack of simulations to estimate this quantity accurately, and define a loss function that measures this uncertainty. We then propose to select the next evaluation location to minimise the expected loss. Experiments show that the proposed method often produces the most accurate approximations as compared to common BO strategies.