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

J. Kudlicka, L. M. Murray, T. B. Schön, F. Lindsten

In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). 2020

Abstract

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.

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BibTeX entry

@inproceedings{,
    title={Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed sampling},
    author={Kudlicka, Jan and Murray, Lawrence M. and Sch\"on, Thomas B. and Lindsten, Fredrik},
    booktitle={IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
    year={2020},
    pages={5860-5864}
}