Implicit particle filters for data assimilation

Abstract

Implicit particle filters for data assimilation update the particles by first choosing probabilities and then looking for particle locations that assume them, guiding the particles one by one to the high probability domain. We provide a detailed description of these filters, with illustrative examples, together with new, more general, methods for solving the algebraic equations and with a new algorithm for parameter identification.

References in corpus (3)

• Sharp failure rates for the bootstrap particle filter in high dimensions
• Efficient Monte Carlo sampling by parallel marginalization
• Interpolation and Iteration for Nonlinear Filters

Cited by in corpus (2)

• A McKean optimal transportation perspective on Feynman-Kac formulae with application to data assimilation
• Ensemble filter techniques for intermittent data assimilation - a survey