English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

We present a new approach-the ALVar estimator-to estimation of asymptoticvariance in sequential Monte Carlo methods, or, particle filters. The method,which adjusts adaptively the lag of the estimator proposed in [Olsson, J. andDouc, R. (2019). Numerically stable online estimation of variance in particlefilters. Bernoulli, 25(2), pp. 1504-1535] applies to very general distributionflows and particle filters, including auxiliary particle filters with adaptiveresampling. The algorithm operates entirely online, in the sense that it isable to monitor the variance of the particle filter in real time and with, onthe average, constant computational complexity and memory requirements periteration. Crucially, it does not require the calibration of any algorithmicparameter. Estimating the variance only on the basis of the genealogy of thepropagated particle cloud, without additional simulations, the routine requiresonly minor code additions to the underlying particle algorithm. Finally, weprove that the ALVar estimator is consistent for the true asymptotic varianceas the number of particles tends to infinity and illustrate numerically itssuperiority to existing approaches.

Adaptive online variance estimation in particle filters: the ALVar estimator