Convergence rates for residual branching particle filters
DOI10.1016/j.jmaa.2016.12.046zbMath1376.60076OpenAlexW2564614877MaRDI QIDQ508964
Publication date: 8 February 2017
Published in: Journal of Mathematical Analysis and Applications (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.jmaa.2016.12.046
central limit theorembranching processcouplingsequential Monte CarloMcKean-Vlasov particle systemMarcinkiewicz strong laws of large numbers
Central limit and other weak theorems (60F05) Interacting random processes; statistical mechanics type models; percolation theory (60K35) Large deviations (60F10) Branching processes (Galton-Watson, birth-and-death, etc.) (60J80)
Related Items (2)
Cites Work
- Unnamed Item
- Tracking rapid intracellular movements: a Bayesian random set approach
- On the interrelation of almost sure invariance principles for certain stochastic adaptive algorithms and for partial sums of random variables
- Nonlinear filtering and measure-valued processes
- Particle representations for a class of nonlinear SPDEs
- Residual and stratified branching particle filters
- Microstructure models with short-term inertia and stochastic volatility
- Central limit theorem for sequential Monte Carlo methods and its application to Bayesian inference
- Dynamic filtering of static dipoles in magnetoencephalography
- Rates for branching particle approximations of continuous-discrete filters
- Monte Carlo technique for prediction and filtering of non-linear stochastic processes
- On convergence determining and separating classes of functions
- A martingale approach to central limit theorems for exchangeable random variables
- BAYESIAN MODEL SELECTION VIA FILTERING FOR A CLASS OF MICRO-MOVEMENT MODELS OF ASSET PRICE
- A CLASS OF MARKOV PROCESSES ASSOCIATED WITH NONLINEAR PARABOLIC EQUATIONS
- Monte Carlo techniques to estimate the conditional expectation in multi-stage non-linear filtering†
This page was built for publication: Convergence rates for residual branching particle filters