Data assimilation for large‐scale spatio‐temporal systems using a location particle smoother

DOI10.1002/ENV.2184zbMath1525.62080OpenAlexW1860382590WikidataQ123417524 ScholiaQ123417524MaRDI QIDQ6069053

No author found.

Publication date: 15 December 2023

Published in: Environmetrics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/env.2184

zbMATH Keywords

time seriescopulasstate-space modelsdata assimilationdynamic systemsparticle filteringhigh dimensionalspatiotemporal statistics

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bayesian forecasting and dynamic models.
• The meta-elliptical distributions with given marginals
• A Bayesian tutorial for data assimilation
• Inference in hidden Markov models.
• Stochastic processes and filtering theory
• Curse-of-dimensionality revisited: Collapse of the particle filter in very large scale systems
• On markov chain monte carlo methods for nonlinear and non-gaussian state-space models
• Inference for Deterministic Simulation Models: The Bayesian Melding Approach
• Particle Markov Chain Monte Carlo Methods
• Monte Carlo Smoothing for Nonlinear Time Series
• Handbook of Dynamic System Modeling
• Data Assimilation

This page was built for publication: Data assimilation for large‐scale spatio‐temporal systems using a location particle smoother