Uniform Time Average Consistency of Monte Carlo Particle Filters
arXiv:0812.0350 · doi:10.1016/j.spa.2009.09.004
Abstract
We prove that bootstrap type Monte Carlo particle filters approximate the optimal nonlinear filter in a time average sense uniformly with respect to the time horizon when the signal is ergodic and the particle system satisfies a tightness property. The latter is satisfied without further assumptions when the signal state space is compact, as well as in the noncompact setting when the signal is geometrically ergodic and the observations satisfy additional regularity assumptions.
arXiv comment21 pages, 1 figure
References in corpus (4)
- Recursive Monte Carlo filters: Algorithms and theoretical analysis
- Sequential Monte Carlo smoothing with application to parameter estimation in non-linear state space models
- Uniform observability of hidden Markov models and filter stability for unstable signals
- The stability of conditional Markov processes and Markov chains in random environments
Cited by in corpus (19)
- Sequential Monte Carlo smoothing for general state space hidden Markov models
- Stability properties of some particle filters
- Path storage in the particle filter
- Linear Variance Bounds for Particle Approximations of Time-Homogeneous Feynman-Kac Formulae
- A proof of uniform convergence over time for a distributed particle filter
- Twisted particle filters
- On the Mathematical Theory of Ensemble (Linear-Gaussian) Kalman-Bucy Filtering
- Error Bounds and Normalizing Constants for Sequential Monte Carlo in High Dimensions
- Ergodicity and stability of the conditional distributions of nondegenerate Markov chains
- On the exchange of intersection and supremum of sigma-fields in filtering theory
- Conditional ergodicity in infinite dimension
- Particle-based adaptive-lag online marginal smoothing in general state-space models
- Mixing it up: A general framework for Markovian statistics
- Stable approximation schemes for optimal filters
- The Kalman Like Particle Filter : Optimal Estimation With Quantized Innovations/Measurements
- Learning dynamical systems with particle stochastic approximation EM
- Ergodicity, Decisions, and Partial Information
- Approximating Posterior Predictive Distributions by Averaging Output From Many Particle Filters
- State-Observation Sampling and the Econometrics of Learning Models