Fundamentals of Stochastic Filtering

By Alan Bain, D. Crisan. 2008

689 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering

View Paper: https://www.semanticscholar.org/paper/982308a6e9f5798884ac187848982b9054c0dbe2

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In Civil and Structural Engineering

Citations in the corpus

Robust filtering: Correlated noise and multidimensional observation (2012, methodology): “A similar approach was followed in the proof of Theorem 1 in [3] 11 In the forthcoming publication [9] we show existence of solutions to SDEs with rough drift under additional sets of assumptions.”
Long-term stability of sequential Monte Carlo methods under verifiable conditions (2012, background): “, Lp error bounds and weak convergence—see the monographs [5, 1] and the references therein.”
Online Optimization in Dynamic Environments (2013, background): “(LMS) or recursive least squares (RLS) algorithms do not have a natural mechanism for incorporating dynamics. Classical stochastic ltering methods such as Kalman or particle lters or Bayesian updates [7] readily exploit dynamical models for eective prediction and tracking performance. However, these”
Data assimilation in the geosciences: An overview of methods, issues, and perspectives (2017, background): “…are given below without their derivation, which goes beyond the scope of this overview, but the reader can find numerous alternative ways in classical books or reviews on filtering (Bain & Crisan, 2009; Bengtsson et al., 1981; Cohn, 1997; Jazwinski, 1970; Talagrand, 1997; Wikle & Berliner, 2007).”
Szegö's theorem and its probabilistic descendants (2011, neutral): “[BaiCr].”
Dynamical Models and tracking regret in online convex programming (2013, methodology): “Classical stochastic filtering methods such as Kalman or particle filters or Bayesian updates (Bain & Crisan, 2009) readily exploit dynamical models for effective prediction and tracking performance.”
Filtering Complex Turbulent Systems (2012, background): “Particle filtering of low-dimensional dynamical systems is an established discipline [14].”
Information geometry and its applications (2012, background): “Projection filters Another application of information geometry can be found in filter theory [14], [15].”
Online Convex Optimization in Dynamic Environments (2015, background): “Classical stochastic filteringmethods such as Kalman or particle filters or Bayesian updates [7] readily exploit dynamical models for effective prediction and tracking performance.”