Bayesian-Based Methods for the Estimation of the Unknown Model’s Parameters in the Case of the Localization of the Atmospheric Contamination Source

• Autorzy: Borysiewicz M. , Wawrzynczak A. , Kopka P.
• Języki publikacji: EN

Abstrakty

EN

In many areas of application it is important to estimate unknown model parameters in order to model precisely the underlying dynamics of a physical system. In this context the Bayesian approach is a powerful tool to combine observed data along with prior knowledge to gain a current (probabilistic) understanding of unknown model parameters. We have applied the methodology combining Bayesian inference with Markov chain Monte Carlo (MCMC) to the problem of the atmospheric contaminant source localization. The algorithm input data are the on-line arriving information about concentration of given substance registered by distributed sensor network. We have examined different version of the MCMC algorithms in effectiveness to estimate the probabilistic distributions of atmospheric release parameters. The results indicate the probability of a source to occur at a particular location with a particular release rate.

Słowa kluczowe

EN

Bayesian inference; stochastic reconstruction; MCMC methods

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2012
• Tom: Vol. 37, No. 4
• Strony: 256--272
• Opis fizyczny: Bibliogr. 18 poz.

Twórcy

autor

Borysiewicz M.

• National Centre for Nuclear Research, Swierk - Otwock, Poland

autor

Wawrzynczak A.

• National Centre for Nuclear Research, Swierk - Otwock, Poland
• Institute of Computer Science, Siedlce University, Poland

autor

Kopka P.

• National Centre for Nuclear Research, Swierk - Otwock, Poland
• Institute of Computer Science of the Polish Academy of Sciences, Warsaw, Poland

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