Sequential Monte Carlo methods for filtering of unobservable components of multidimensional diffusion Markov processes
DOI10.1080/23311835.2015.1134031zbMath1426.60045OpenAlexW340672618MaRDI QIDQ4966726
Publication date: 27 June 2019
Published in: Cogent Mathematics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/23311835.2015.1134031
simulationnonlinear filteringparticle filtersvolatilityquadratic variationsequential Monte Carlo methodsmultidimensional diffusion Markov process
Computational methods in Markov chains (60J22) Markov processes: estimation; hidden Markov models (62M05) Monte Carlo methods (65C05) Signal detection and filtering (aspects of stochastic processes) (60G35) Diffusion processes (60J60)
Cites Work
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- Particle filtering: the need for speed
- On the convergence of two sequential Monte Carlo methods for maximum a posteriori sequence estimation and stochastic global optimization
- Measure-valued processes and interacting particle systems. Application to nonlinear filtering problems
- On stochastic differential equations for the a posteriori probability distribution in problems of adaptive filtering and signal detection
- Sequential Monte Carlo Methods in Practice
- Particle methods: An introduction with applications
- A survey of convergence results on particle filtering methods for practitioners
- Robust Monte Carlo localization for mobile robots
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