Particle predictive control

Author:

de Villiers J.P.,Godsill S.J.,Singh S.S.

Publisher

Elsevier BV

Subject

Applied Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability

Reference26 articles.

1. Particle Markov chain Monte Carlo methods (with discussion);Andrieu;Journal of the Royal Statistical Society B,2010

2. Feynman-Kac Formulae: Genealogical and Interacting Particle Systems with Applications;Del Moral,2004

3. Doucet, A., 1998. On sequential simulation-based methods for Bayesian filtering. Technical Report CUED-F-ENG-TR310, University of Cambridge, Department of Engineering 〈www.stats.bris.uk/MCMC/〉.

4. On sequential Monte Carlo sampling methods for Bayesian filtering;Doucet;Statistics and Computing,2000

5. Marginal maximum a posteriori estimation using MCMC;Doucet;Statistics and Computing,2002

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2. Testing trajectory of road trains with program complexes;The Archives of Automotive Engineering – Archiwum Motoryzacji;2019-03-29

3. A non-linear estimation and model predictive control algorithm based on ant colony optimization;Transactions of the Institute of Measurement and Control;2019-02

4. Nonlinear stochastic receding horizon control: stability, robustness and Monte Carlo methods for control approximation;International Journal of Control;2017-07-28

5. Online motion synthesis using sequential Monte Carlo;ACM Transactions on Graphics;2014-07-27

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