Adapting the Number of Particles in Sequential Monte Carlo Methods Through an Online Scheme for Convergence Assessment

DOI10.1109/TSP.2016.2637324zbMath1414.94183arXiv1509.04879OpenAlexW2341285855MaRDI QIDQ4620705

Víctor Elvira, Joaquín Míguez, Petar M. Djurić

Publication date: 8 February 2019

Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1509.04879

Mathematics Subject Classification ID

Signal theory (characterization, reconstruction, filtering, etc.) (94A12)

Related Items (3)

Automatically adapting the number of state particles in \(\text{SMC}^2\) ⋮ Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization ⋮ On the performance of particle filters with adaptive number of particles

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