Uniform convergence over time of a nested particle filtering scheme for recursive parameter estimation in state-space Markov models

DOI10.1017/apr.2017.38zbMath1432.62320arXiv1603.09005OpenAlexW2963231450MaRDI QIDQ5233205

Joaquín Míguez, Dan Crisan

Publication date: 16 September 2019

Published in: Advances in Applied Probability (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1603.09005

zbMATH Keywords

parameter estimationuniform convergenceMonte Carlo integrationstate-space modelmodel inferenceparticle filtering

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Bayesian inference (62F15) Monte Carlo methods (65C05)

Related Items (4)

Automatically adapting the number of state particles in \(\text{SMC}^2\) ⋮ Nudging the particle filter ⋮ On the performance of particle filters with adaptive number of particles ⋮ Uniform convergence over time of a nested particle filtering scheme for recursive parameter estimation in state-space Markov models

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