2 research outputs found
Adapting the Number of Particles in Sequential Monte Carlo Methods through an Online Scheme for Convergence Assessment
Particle filters are broadly used to approximate posterior distributions of
hidden states in state-space models by means of sets of weighted particles.
While the convergence of the filter is guaranteed when the number of particles
tends to infinity, the quality of the approximation is usually unknown but
strongly dependent on the number of particles. In this paper, we propose a
novel method for assessing the convergence of particle filters online manner,
as well as a simple scheme for the online adaptation of the number of particles
based on the convergence assessment. The method is based on a sequential
comparison between the actual observations and their predictive probability
distributions approximated by the filter. We provide a rigorous theoretical
analysis of the proposed methodology and, as an example of its practical use,
we present simulations of a simple algorithm for the dynamic and online
adaption of the number of particles during the operation of a particle filter
on a stochastic version of the Lorenz system