Langevin and Hamiltonian Based Sequential MCMC for Efficient Bayesian Filtering in High-Dimensional Spaces

@article{Septier2016LangevinAH,
  title={Langevin and Hamiltonian Based Sequential MCMC for Efficient Bayesian Filtering in High-Dimensional Spaces},
  author={François Septier and Gareth W. Peters},
  journal={IEEE Journal of Selected Topics in Signal Processing},
  year={2016},
  volume={10},
  pages={312-327}
}
Nonlinear non-Gaussian state-space models arise in numerous applications in statistics and signal processing. In this context, one of the most successful and popular approximation techniques is the Sequential Monte Carlo (SMC) algorithm, also known as particle filtering. Nevertheless, this method tends to be inefficient when applied to high dimensional problems. In this paper, we focus on another class of sequential inference methods, namely the Sequential Markov Chain Monte Carlo (SMCMC… CONTINUE READING
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References

Publications referenced by this paper.
Showing 1-10 of 63 references

An overview of recent advances inMonte- Carlo methods for Bayesian filtering in high-dimensional spaces

F. Septier andG.W. Peters
Theoretical Aspects of Spatial-Temporal Modeling, G. W. Peters and T. Matsui, Eds. New York, NY, USA: SpringerBriefs—JSS Research Series in Statistics, 2015. • 2015
View 7 Excerpts
Highly Influenced

MCMC using Hamiltonian dynamics

View 4 Excerpts
Highly Influenced

Following a moving target-Monte Carlo inference for dynamic Bayesian models

W. R. Gilks, C. Berzuini
J. Roy Statist. Soc. Series B Statist. Methodol., vol. 63, pp. 127–146, 2001. • 2001
View 4 Excerpts
Highly Influenced

Quantitative Risk Management

International Encyclopedia of Statistical Science • 2011
View 3 Excerpts
Highly Influenced

Unscented filtering and nonlinear estimation

Proceedings of the IEEE • 2004
View 2 Excerpts
Highly Influenced

Proximal Markov chain Monte Carlo algorithms

Statistics and Computing • 2016
View 1 Excerpt

Bayesian computation: A perspective on the current state, sampling backwards and forwards

P. J. Green, K. Latuszynski, M. Pereyra, C. P. Robert
arXiv.org, Feb. 2015. • 2015
View 1 Excerpt

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