# Optimal proposals for Approximate Bayesian Computation

arXiv: https://arxiv.org/abs/1808.06040 · HTML: https://mlsanity.com/paper/1808.06040/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2018.

## Built on by (most cited first, 1 of 1)

- [Tree Bandits for Generative Bayes](https://mlsanity.com/paper/2404.10436/) (2024)

## Abstract (arXiv)

We derive the optimal proposal density for Approximate Bayesian Computation (ABC) using Sequential Monte Carlo (SMC) (or Population Monte Carlo, PMC). The criterion for optimality is that the SMC/PMC-ABC sampler maximise the effective number of samples per parameter proposal. The optimal proposal density represents the optimal trade-off between favoring high acceptance rate and reducing the variance of the importance weights of accepted samples. We discuss two convenient approximations of this proposal and show that the optimal proposal density gives a significant boost in the expected sampling efficiency compared to standard kernels that are in common use in the ABC literature, especially as the number of parameters increases.