On the performance of particle filters with adaptive number of particles

DOI10.1007/s11222-021-10056-0zbMath1478.62006arXiv1911.01383OpenAlexW3210967620MaRDI QIDQ2066734

Joaquín Míguez, Petar M. Djurić, Víctor Elvira

Publication date: 14 January 2022

Published in: Statistics and Computing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1911.01383

zbMATH Keywords

convergence analysispredictive distributionssequential Monte Carloparticle filteringadaptive complexity

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Inference from stochastic processes and prediction (62M20) Filtering in stochastic control theory (93E11) Monte Carlo methods (65C05)

Related Items

Automatically adapting the number of state particles in \(\text{SMC}^2\)

Uses Software

• BookIndRandSamplingMethods

Cites Work

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• SMC2: An Efficient Algorithm for Sequential Analysis of State Space Models
• On the stability of interacting processes with applications to filtering and genetic algorithms

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