• Journal of the Korean Data and Information Science Society
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• v.20 no.1
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• pp.39-48
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• 2009

Fisher information matrix plays an important role in statistical inference of unknown parameters. Especially, it is used in objective Bayesian inference which derives to the posterior distribution using a noninformative prior distribution and is an example of metric functions in geometry. The more parameters for estimating in a distribution are, the more complicate derivation of the Fisher information matrix for the distribution is. In this paper, we derive to the Fisher information matrix for 3-parameters Weibull distribution which is used in reliability theory using Mathematica programs.

• Journal of the Korean Data and Information Science Society
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• v.16 no.1
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• pp.71-78
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• 2005

Recently an important issue in Bayesian methodology is determination of noninformative prior distributions, often required when there is no idea of prior information. In this thesis attention is focused on the development of noninformative priors for stationary AR(1) model. The noninformative priors primarily discussed are the Jeffreys prior, and the reference priors. The remarkable points in the result are that the Jeffreys prior coincides with the reference prior for the case that $\rho$ is the parameter of interest.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.29-36
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• 2006

This paper proposes a new algorithm of MS-EIDV (modal sensitivity-effective independence distribution vector) for determining optimal accelerometer locations (OAL) by using the Fisher Information Matrix (FIM) derived from mode-shape sensitivities. Also, the paper provides a reasonable guideline for selecting OAL which can reflect dynamic responses of a structure effectively. Since OAL should be determined with known values of structural parameters but since the parameters can be estimated by applying an inverse method such as SI (system identification) using measured response, the paper proposes a statistical method to overcome the paradox by considering the error bound of the structural parameters. To examine the proposed methods, a frequency-domain SI method has been applied. By using the identified results, the minimum necessary number of accelerometers could be selected depending on the number of target measurable modes. Through simulation studies, the results by applying EIDV method directly using the information of mode shapes were compared with those by applying the proposed MS-EIDV.