Bayesian system identification via Markov chain Monte Carlo techniques

@article{Ninness2010BayesianSI,
  title={Bayesian system identification via Markov chain Monte Carlo techniques},
  author={Brett Ninness and Soren J. Henriksen},
  journal={Automatica},
  year={2010},
  volume={46},
  pages={40-51}
}
The work here explores new numerical methods for supporting a Bayesian approach to parameter estimation of dynamic systems. This is primarily motivated by the goal of providing accurate quantification of estimation error that is valid for arbitrary, and hence even very short length data records. The main innovation is the employment of the Metropolis–Hastings algorithm to construct an ergodic Markov chain with invariant density equal to the required posterior density. Monte–Carlo analysis of… CONTINUE READING
Highly Cited
This paper has 74 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 43 extracted citations

75 Citations

0102030'10'12'14'16'18
Citations per Year
Semantic Scholar estimates that this publication has 75 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 41 references

Bayesians

  • B. Efron
  • frequentists and scientists, Am. Stat. Assoc…
  • 2004
1 Excerpt

Subjective Probability:The real thing

  • R. Jeffrey
  • Cambridge University Press
  • 2004
2 Excerpts

A computational Bayesian approach to system identification

  • B. Ninness, S. Henriksen
  • Proceedings 13th IFAC Symposium on System…
  • 2003
1 Excerpt

Similar Papers

Loading similar papers…