BAYESIAN-BASED METHODS FOR THE ESTIMATION OF THE UNKNOWN MODEL'S PARAMETERS IN THE CASE OF THE LOCALIZATION OF THE ATMOSPHERIC CONTAMINATION SOURCE

被引:25
作者
Borysiewicz, M. [1 ]
Wawrzynczak, A. [1 ,2 ]
Kopka, P. [1 ,3 ]
机构
[1] Natl Ctr Nucl Res, Otwock, Poland
[2] Siedlce Univ, Inst Comp Sci, Siedlce, Poland
[3] Polish Acad Sci, Inst Comp Sci, Warsaw, Poland
关键词
Bayesian inference; stochastic reconstruction; MCMC methods;
D O I
10.2478/v10209-011-0014-9
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:253 / 270
页数:18
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