Adaptive kernels in approximate filtering of state‐space models
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Publication:4976368
DOI10.1002/acs.2739zbMath1367.93642OpenAlexW2556955972MaRDI QIDQ4976368
Publication date: 28 July 2017
Published in: International Journal of Adaptive Control and Signal Processing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1002/acs.2739
nonlinear filtersfilteringsequential Monte Carloparticle filterBayesian filteringapproximate filtering
Filtering in stochastic control theory (93E11) Adaptive control/observation systems (93C40) Discrete-time control/observation systems (93C55) Estimation and detection in stochastic control theory (93E10)
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Cites Work
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- Particle approximations of the score and observed information matrix in state space models with application to parameter estimation
- Bayesian forecasting and dynamic models.
- An adaptive sequential Monte Carlo method for approximate Bayesian computation
- Approximate Bayesian computational methods
- Filtering via approximate Bayesian computation
- A note on auxiliary particle filters
- Resampling algorithms for particle filters: a computational complexity perspective
- New insights into approximate Bayesian computation
- Nonlinear data assimilation
- Recursive Monte Carlo filters: algorithms and theoretical analysis
- Gradient free parameter estimation for hidden Markov models with intractable likelihoods
- Sequential Monte Carlo Methods in Practice
- Sequential Monte Carlo Methods for Nonlinear Discrete-Time Filtering
- Parameter Estimation for Hidden Markov Models with Intractable Likelihoods
- How to exploit external model of data for parameter estimation?
- Filtering via Simulation: Auxiliary Particle Filters
- A Particle Filtering Scheme for Processing Time Series Corrupted by Outliers
- Gaussian sum particle filtering
- Error Bounds and Normalising Constants for Sequential Monte Carlo Samplers in High Dimensions
- Approximate Bayesian Computation for Smoothing
- Bayesian computation: a summary of the current state, and samples backwards and forwards