• 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 Korea institute for structural maintenance and inspection
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• v.16 no.5
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• pp.29-39
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• 2012

The main objective is to predict the future degradation and maintenance budget for a suspension bridge system. Bayesian inference is applied to find the posterior probability density function of the source parameters (damage indices and serviceability), given ten years of maintenance data. The posterior distribution of the parameters is sampled using a Markov chain Monte Carlo method. The simulated risk prediction for decreased serviceability conditions are posterior distributions based on prior distribution and likelihood of data updated from annual maintenance tasks. Compared with conventional linear prediction model, the proposed quadratic model provides highly improved convergence and closeness to measured data in terms of serviceability, risky factors, and maintenance budget for bridge components, which allows forecasting a future performance and financial management of complex infrastructures based on the proposed quadratic stochastic regression model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1997.10a
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• pp.193-196
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• 1997

In this paper we consider a Bayesian theoretic approach to periodic incomplete preventive maintenance with minimal repair at failure. We assume that the system failure rate is increasing as the frequency of PM increases and that the system is replaced at the K-th PM under this maintenance strategy. The optimal policies which minimize the expected cost rates are discussed. We seek the optimal periodic PM interval x and replacement time K under a Weibull failure intensity. Assuming suitable prior distribution for the Weibull parameters, we derive the posterior distribution incorporating failure data and obtain the updated optimal replacement strategies.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.7
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• pp.1750-1758
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• 2015

This paper provides Bayesian methodology to estimate the reliability for guided weapons which are not continuously operating. The posterior distribution of subsystems and components becomes the next prior distribution. By analyzing the results of the sub-systems and components presented a method for estimating the reliability of the entire guided weapons. Bayesian methodology using existing test data of subsystems may be used to reduce the sample sizes.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.501-509
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• 2009

Recently the interval estimation of binomial proportions is revisited in various literatures. This is mainly due to the erratic behavior of the coverage probability of the well-known Wald confidence interval. Various alternatives have been proposed. Among them, the Agresti-Coull confidence interval, the Wilson confidence interval and the Bayes confidence interval resulting from the noninformative Jefferys prior were recommended by Brown et al. (2001). However, unlike the binomial distribution case, little is known about the properties of the confidence intervals in finite population sampling. In this note, the property of confidence intervals is investigated in anile population sampling.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.1
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• pp.40-48
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• 2007

This paper presents a novel texture segmentation method using multilayer perceptron (MLP) networks and Markov random fields in multiscale Bayesian framework. Multiscale wavelet coefficients are used as input for the neural networks. The output of the neural network is modeled as a posterior probability. Texture classification at each scale is performed by the posterior probabilities from MLP networks and MAP (maximum a posterior) classification. Then, in order to obtain the more improved segmentation result at the finest scale, our proposed method fuses the multiscale MAP classifications sequentially from coarse to fine scales. This process is done by computing the MAP classification given the classification at one scale and a priori knowledge regarding contextual information which is extracted from the adjacent coarser scale classification. In this fusion process, the MRF (Markov random field) prior distribution and Gibbs sampler are used, where the MRF model serves as the smoothness constraint and the Gibbs sampler acts as the MAP classifier. The proposed segmentation method shows better performance than texture segmentation using the HMT (Hidden Markov trees) model and HMTseg.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.17 no.3
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• pp.251-259
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• 2004

The prediction accuracy of prestress plays an important role in the quality of maintenance and the decision on rehabilitation of infrastructure such as prestressed concrete bridges. In this paper, the Bayesian statistical method that uses in-situ measurement data for reducing the uncertainties or updating long-term prediction of prestress is presented. For Bayesian analysis, prior probability distribution is developed to represent the uncertainties of creep and shrinkage of concrete and likelihood function is derived and used with data acquired in site. Posterior probability distribution is then obtained by combining prior distribution and likelihood function. The numerical results of this study indicate that more accurate long-term prediction of prestress forces due to creep and shrink age is possible.

• Journal of the Korea institute for structural maintenance and inspection
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• v.14 no.5
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• pp.119-127
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• 2010

This paper provides a new approach for durability prediction of reinforced concrete structures exposed to carbonation. In this method, the prediction can be updated successively by a Bayes' theorem when additional data are available. The stochastic properties of model parameters are explicitly taken into account in the model. To simplify the procedure of the model, the probability of the durability limit is determined based on the samples obtained from the Latin Hypercube Sampling(LHS) technique. The new method may be very useful in design of important concrete structures and help to predict the remaining service life of existing concrete structures which have been monitored. For using the new method, in which the prior distribution is developed to represent the uncertainties of the carbonation velocity using data of concrete structures(3700 specimens) in Korea and the likelihood function is used to monitor in-situ data. The posterior distribution is obtained by combining a prior distribution and a likelihood function. Efficiency of the LHS technique for simulation was confirmed through a comparison between the LHS and the Monte Calro Simulation(MCS) technique.

• Convergence Security Journal
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• v.6 no.3
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• pp.117-126
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• 2006

Bayesian inference and model selection method for software reliability growth models are studied. Software reliability growth models are used in testing stages of software development to model the error content and time intervals between software failures. In this paper, could avoid multiple integration using Gibbs sampling, which is a kind of Markov Chain Monte Carlo method to compute the posterior distribution. Bayesian inference for general order statistics models in software reliability with diffuse prior information and model selection method are studied. For model determination and selection, explored goodness of fit (the error sum of squares), trend tests. The methodology developed in this paper is exemplified with a software reliability random data set introduced by of Weibull distribution(shape 2 & scale 5) of Minitab (version 14) statistical package.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.197-207
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• 2015

In baseball game, the hitting ability of batter is frequently assessed by a batting average, a run batted in, a home run, a run scored, an on-base percentage, etc. Recently, more comprehensive indicators such as OPS, ISO, SECA, TA, RC and XR are often used. But, these measures generally shows large deviations since they are calculated from the data for a certain period of time, and they are not an estimate of a population parameter, either. In this paper, we will presume the pure hitting ability of the korea professional baseball players as a parameter which is depend upon at bat. We will estimate the parameter by using the Bayesian method.