Localization in High-Dimensional Monte Carlo Filtering

DOI10.1007/978-3-319-54084-9_8zbMath1364.62270arXiv1610.03701OpenAlexW2531380964MaRDI QIDQ5267858

Sylvain Robert, Hans R. Künsch

Publication date: 13 June 2017

Published in: Springer Proceedings in Mathematics & Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1610.03701

zbMATH Keywords

curse of dimensionalitydata assimilationparticle filterensemble Kalman filterMonte Carlo filteringfiltering in high dimensionlocalized filter algorithms

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Applications of statistics to environmental and related topics (62P12) Monte Carlo methods (65C05)

Uses Software

• EnKF

Cites Work

• Unnamed Item
• Can local particle filters beat the curse of dimensionality?
• Recursive Monte Carlo filters: algorithms and theoretical analysis
• Efficient data assimilation for spatiotemporal chaos: a local ensemble transform Kalman filter
• Sequential Monte Carlo Methods in Practice
• Bridging the ensemble Kalman and particle filters
• Filtering via Simulation: Auxiliary Particle Filters

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