Variance estimation for Sequential Monte Carlo Algorithms: a backward sampling approach - Archive ouverte HAL
Journal Articles Bernoulli Year : 2024

Variance estimation for Sequential Monte Carlo Algorithms: a backward sampling approach

Abstract

In this paper, we consider the problem of online asymptotic variance estimation for particle filtering and smoothing. Current solutions for the particle filter rely on the particle genealogy and are either unstable or hard to tune in practice. We propose to mitigate these limitations by introducing a new estimator of the asymptotic variance based on the so called backward weights. The resulting estimator is weakly consistent and trades computational cost for more stability and reduced variance. We also propose a more computationally efficient estimator inspired by the PaRIS algorithm of [33]. As an application, particle smoothing is considered and an estimator of the asymptotic variance of the Forward Filtering Backward Smoothing estimator applied to additive functionals is provided.
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Dates and versions

hal-03630333 , version 1 (05-04-2022)
hal-03630333 , version 2 (10-01-2023)

Identifiers

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Yazid Janati, Sylvain Le Corff, Yohan Petetin. Variance estimation for Sequential Monte Carlo Algorithms: a backward sampling approach. Bernoulli, 2024, 30 (2), ⟨10.3150/23-BEJ1586⟩. ⟨hal-03630333v2⟩
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