Bias of Particle Approximations to Optimal Filter Derivative
From MaRDI portal
Publication:5853635
DOI10.1137/18M1217024zbMath1456.93006arXiv1806.09590OpenAlexW3133040484WikidataQ114074286 ScholiaQ114074286MaRDI QIDQ5853635
Arnaud Doucet, Vladislav Z. B. Tadić
Publication date: 11 March 2021
Published in: SIAM Journal on Control and Optimization (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1806.09590
Filtering in stochastic control theory (93E11) Control/observation systems governed by partial differential equations (93C20)
Cites Work
- Unnamed Item
- Unnamed Item
- Particle approximations of the score and observed information matrix in state space models with application to parameter estimation
- On particle methods for parameter estimation in state-space models
- Stability and uniform approximation of nonlinear filters using the Hilbert metric and application to particle filters
- A sharp first order analysis of Feynman-Kac particle models. I: Propagation of chaos
- Asymptotic bias of stochastic gradient search
- Asymptotic properties of the maximum likelihood estimator in autoregressive models with Markov regime
- Exponential forgetting and geometric ergodicity in hidden Markov models
- Particle-based online estimation of tangent filters with application to parameter estimation in nonlinear state-space models
- Exponential forgetting and geometric ergodicity for optimal filtering in general state-space models
- Inference in hidden Markov models.
- Sequential Monte Carlo Methods in Practice
- Discrete Probability Models and Methods
- Mean Field Simulation for Monte Carlo Integration
- Feynman-Kac Particle Integration with Geometric Interacting Jumps
- A backward particle interpretation of Feynman-Kac formulae
- Uniform Stability of a Particle Approximation of the Optimal Filter Derivative
- Stability and Uniform Particle Approximation of Nonlinear Filters in Case of Non Ergodic Signals
- Sharp Propagation of Chaos Estimates for Feynman–Kac Particle Models
- On the stability of interacting processes with applications to filtering and genetic algorithms
This page was built for publication: Bias of Particle Approximations to Optimal Filter Derivative
