# Particle filter with rejection control and unbiased estimator of the marginal likelihood

arXiv: https://arxiv.org/abs/1910.09527 · HTML: https://mlsanity.com/paper/1910.09527/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2019.

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

- [Variational Rejection Particle Filtering](https://mlsanity.com/paper/2103.15343/) (2021)

## Abstract (arXiv)

We consider the combined use of resampling and partial rejection control in sequential Monte Carlo methods, also known as particle filters. While the variance reducing properties of rejection control are known, there has not been (to the best of our knowledge) any work on unbiased estimation of the marginal likelihood (also known as the model evidence or the normalizing constant) in this type of particle filter. Being able to estimate the marginal likelihood without bias is highly relevant for model comparison, computation of interpretable and reliable confidence intervals, and in exact approximation methods, such as particle Markov chain Monte Carlo. In the paper we present a particle filter with rejection control that enables unbiased estimation of the marginal likelihood.