A Unification of Weighted and Unweighted Particle Filters
DOI10.1137/20M1382404zbMath1491.60054arXiv2011.13804MaRDI QIDQ5065052
Ehsan Abedi, Jean-Pascal Pfister, Simone Carlo Surace
Publication date: 18 March 2022
Published in: SIAM Journal on Control and Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2011.13804
Fokker-Planck equationimportance samplingstochastic differential equationsnonlinear filteringinteracting particle systemsKushner-Stratonovich equationMcKean-Vlasov processes
Filtering in stochastic control theory (93E11) Monte Carlo methods (65C05) Signal detection and filtering (aspects of stochastic processes) (60G35) Stochastic partial differential equations (aspects of stochastic analysis) (60H15) Stochastic particle methods (65C35)
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Multivariable feedback particle filter
- Fundamentals of stochastic filtering
- Some geometric calculations on Wasserstein space
- The Hitchhiker's guide to nonlinear filtering
- Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference
- Estimation of the derivative of the logarithm of a density
- Conditional Markov Processes
- How to Avoid the Curse of Dimensionality: Scalability of Particle Filters with and without Importance Weights
- Applied Stochastic Differential Equations
- An Optimal Transport Formulation of the Ensemble Kalman Filter
- McKean--Vlasov SDEs in Nonlinear Filtering
- Data assimilation: The Schrödinger perspective
- On the Differential Equations Satisfied by Conditional Probablitity Densities of Markov Processes, with Applications
- Feedback Particle Filter
- Estimation of a Probability Density Function and Its Derivatives
This page was built for publication: A Unification of Weighted and Unweighted Particle Filters
