Convergence of empirical processes for interacting particle systems with applications to nonlinear filtering

DOI10.1023/A:1007743111861zbMath1425.60039MaRDI QIDQ5919594

Pierre Del Moral, Michel Ledoux

Publication date: 22 August 2019

Published in: Journal of Theoretical Probability (Search for Journal in Brave)

zbMATH Keywords

empirical processesinteracting particle systemsGlivenko-Cantelli and Donsker theorems

Mathematics Subject Classification ID

Strong limit theorems (60F15) Signal detection and filtering (aspects of stochastic processes) (60G35) Interacting random processes; statistical mechanics type models; percolation theory (60K35) Functional limit theorems; invariance principles (60F17)

Cites Work

• Measure-valued processes and interacting particle systems. Application to nonlinear filtering problems
• Central limit theorem for nonlinear filtering and interacting particle systems
• Sharper bounds for Gaussian and empirical processes
• The weighted bootstrap
• Modeling genetic algorithms with Markov chains.
• Nonlinear filtering and measure-valued processes
• Large deviations for interacting particle systems: Applications to non-linear filtering
• Weak convergence and empirical processes. With applications to statistics
• Stochastic processes and filtering theory
• Conditional Markov Processes
• Exact finite-dimensional filters for certain diffusions with nonlinear drift
• Convergence of a Branching Particle Method to the Solution of the Zakai Equation
• Nonlinear filtering: Interacting particle resolution
• Convergence of stochastic processes
• The Monte-Carlo method for filtering with discrete-time observations
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: Convergence of empirical processes for interacting particle systems with applications to nonlinear filtering