Bayesian system identification via Markov chain Monte Carlo techniques

  title={Bayesian system identification via Markov chain Monte Carlo techniques},
  author={Brett Ninness and Soren J. Henriksen},
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
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Publications referenced by this paper.
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  • 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

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