Reversible jump MCMC for two-state multivariate Poisson mixtures

Jani Lahtinen; Jouko Lampinen

Kybernetika (2003)

  • Volume: 39, Issue: 3, page [307]-315
  • ISSN: 0023-5954

Abstract

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The problem of identifying the source from observations from a Poisson process can be encountered in fault diagnostics systems based on event counters. The identification of the inner state of the system must be made based on observations of counters which entail only information on the total sum of some events from a dual process which has made a transition from an intact to a broken state at some unknown time. Here we demonstrate the general identifiability of this problem in presence of multiple counters.

How to cite

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Lahtinen, Jani, and Lampinen, Jouko. "Reversible jump MCMC for two-state multivariate Poisson mixtures." Kybernetika 39.3 (2003): [307]-315. <http://eudml.org/doc/33645>.

@article{Lahtinen2003,
abstract = {The problem of identifying the source from observations from a Poisson process can be encountered in fault diagnostics systems based on event counters. The identification of the inner state of the system must be made based on observations of counters which entail only information on the total sum of some events from a dual process which has made a transition from an intact to a broken state at some unknown time. Here we demonstrate the general identifiability of this problem in presence of multiple counters.},
author = {Lahtinen, Jani, Lampinen, Jouko},
journal = {Kybernetika},
keywords = {Bayesian inference; fault diagnostics; Poisson processes; reversible-jump MCMC; Bayesian inference; fault diagnostics; Poisson process},
language = {eng},
number = {3},
pages = {[307]-315},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Reversible jump MCMC for two-state multivariate Poisson mixtures},
url = {http://eudml.org/doc/33645},
volume = {39},
year = {2003},
}

TY - JOUR
AU - Lahtinen, Jani
AU - Lampinen, Jouko
TI - Reversible jump MCMC for two-state multivariate Poisson mixtures
JO - Kybernetika
PY - 2003
PB - Institute of Information Theory and Automation AS CR
VL - 39
IS - 3
SP - [307]
EP - 315
AB - The problem of identifying the source from observations from a Poisson process can be encountered in fault diagnostics systems based on event counters. The identification of the inner state of the system must be made based on observations of counters which entail only information on the total sum of some events from a dual process which has made a transition from an intact to a broken state at some unknown time. Here we demonstrate the general identifiability of this problem in presence of multiple counters.
LA - eng
KW - Bayesian inference; fault diagnostics; Poisson processes; reversible-jump MCMC; Bayesian inference; fault diagnostics; Poisson process
UR - http://eudml.org/doc/33645
ER -

References

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  3. Green P., 10.1093/biomet/82.4.711, Biometrika 82 (1995), 711–732,http://www.stats.bris.ac.uk/pub/reports/MCMC/revjump.ps (1995) MR1380810DOI10.1093/biomet/82.4.711
  4. Marrs A. D., An application of Reversible–Jump MCMC to multivariate spherical Gaussian mixtures, In: Advances in Neural Information Processing Systems 10 (M. I. Jordan, M. J. Kearns, and S. A. Solla, eds.), MIT Press, Cambridge, MA 1998 
  5. Neal R. M.//Probabilistic Inference Using Markov Chain Monte Carlo Methods, Technical Report No. CRG-TR-93-1, Dept. of Computer Science, University of Toronto, 1993, ftp:, ftp.cs.toronto.edu/pub/radford/review.ps.Z 
  6. Robert C. P., Casella G., Monte Carlo Statistical Methods, Springer–Verlag, New York 1999 Zbl1096.62003MR1707311
  7. Viallefont V., Richardson, S., Green P., 10.1080/10485250211383, J. Nonparametric Statistics 14 (2002), 1-2, 181–202 Zbl1014.62035MR1905593DOI10.1080/10485250211383

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