• Journal of the Korean Data and Information Science Society
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• v.20 no.3
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• pp.601-614
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• 2009

This paper derives noninformative priors for scale parameter of exponential distribution when the data are collected in multiple step stress accelerated life tests. We nd the objective priors for this model and show that the reference prior satisfies first order matching criterion. Also, we show that there exists no second order matching prior. Some simulation results are given and using artificial data, we perform Bayesian analysis for proposed priors.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.8 no.2
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• pp.116-120
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• 2008

Clustering is a method for unsupervised learning. General clustering tools have been depended on statistical methods and machine learning algorithms. One of the popular clustering algorithms based on machine learning is the self organizing map(SOM). SOM is a neural networks model for clustering. SOM and extended SOM have been used in diverse classification and clustering fields such as data mining. But, SOM has had a problem determining optimal number of clusters. In this paper, we propose an improvement of SOM using gap statistic and probability distribution. The gap statistic was introduced to estimate the number of clusters in a dataset. We use gap statistic for settling the problem of SOM. Also, in our research, weights of feature nodes are updated by probability distribution. After complete updating according to prior and posterior distributions, the weights of SOM have probability distributions for optima clustering. To verify improved performance of our work, we make experiments compared with other learning algorithms using simulation data sets.

• Journal of Korean Society for Quality Management
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• v.42 no.2
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• pp.199-208
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• 2014

Purpose: This paper explains how to obtain the Bayes estimates of the whole launch vehicle and of a vehicle stage, respectively, for a newly developed expendable launch vehicle. Methods: We determine the parameters of the beta prior distribution using the upper bound of the 60% Clopper-Pearson confidence interval of failure probability which is calculated from previous launch data considering the experience of the developer. Results: Probability that a launch vehicle developed from an inexperienced developer succeeds in the first launch is obtained by about one third, which is much smaller than that estimated from the previous research. Conclusion: The proposed approach provides a more conservative estimate than the previous noninformative prior, which is more reasonable especially for the initial reliability of a new vehicle which is developed by an inexperienced developer.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1391-1396
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• 2008

In this paper, we consider two components system which the lifetimes have Marshall and Olkin's bivariate exponential model with bivariate type I censored data. We propose a Bayesian independent test procedure for above model using fractional Bayes factor method by O'Hagan based on improper prior distributions. And we compute the fractional Bayes factor and the posterior probabilities for the hypotheses, respectively. Also we select a hypothesis which has the largest posterior probability. Finally a numerical example is given to illustrate our Bayesian testing procedure.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1183-1197
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• 2011

The prior distribution is the probability distribution we have before observing data. Using Bayes' rule, we can compute the posterior distribution, the new probability distribution, after observing data. Computing the posterior distribution is much easier than before by using Excel VBA macro. In addition, we can conveniently compute the successive updating posterior distributions after observing the independent and sequential outcomes. In this paper we compose some Excel VBA macros for applying Bayes' rule and give some examples.

• The Korean Journal of Food And Nutrition
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• v.29 no.6
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• pp.952-961
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• 2016

The objective of this study is to examine the effects of consumer beliefs regarding three food certifications on their behavioral intention and the behavioral intention biases to purchase (purchasing intention biases) certified agricultural products as predicted by a subjective probability model. The food certifications used for this study are 'Organic food', 'Traceability system of food products,' and 'Hazard Analysis Critical Control Point (HACCP)'. Tofu (bean curd) was selected as being representative of agricultural food products, for the purposes of this study. In 2016, we surveyed 243 consumers regarding the strength of their belief regarding their prior beliefs relative to each certification, as well as the strength of their intention to purchase certified tofu based on their belief strengths for this study. The study resulted in the following findings: Firstly, consumers hold more than two different prior beliefs for each of the three certifications included in this study. Consumers' prior beliefs regarding these certifications have an impact on their consideration as to whether they plan to buy those certified agricultural products. Secondly, consumers try to persuade themselves to ensure that their particular belief about the product's certification could lead to a purchasing decision regarding that agricultural product.

• Journal of Environmental Science International
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• v.20 no.12
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• pp.1541-1551
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• 2011

This study applied the Bayesian method for the quantification of the parameter uncertainty of spatial linear mixed model in the estimation of the spatial distribution of probability rainfall. In the application of Bayesian method, the prior sensitivity analysis was implemented by using the priors normally selected in the existing studies which applied the Bayesian method for the puppose of assessing the influence which the selection of the priors of model parameters had on posteriors. As a result, the posteriors of parameters were differently estimated which priors were selected, and then in the case of the prior combination, F-S-E, the sizes of uncertainty intervals were minimum and the modes, means and medians of the posteriors were similar to the estimates using the existing classical methods. From the comparitive analysis between Bayesian and plug-in spatial predictions, we could find that the uncertainty of plug-in prediction could be slightly underestimated than that of Bayesian prediction.

• Smart Structures and Systems
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• v.33 no.1
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• pp.17-26
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• 2024

The new metro crossing the existing metro will cause the settlement or floating of the existing structures, which will have safety problems for the operation of the existing metro and the construction of the new metro. Therefore, it is necessary to monitor and predict the settlement of the existing metro caused by the construction of the new metro in real time. Considering the complexity and uncertainty of metro settlement, a Gaussian Prior Bayesian Emulator (GPBE) probability prediction model based on Bayesian optimization (BO) is proposed, that is, BO-GPBE. Firstly, the settlement monitoring data are analyzed to get the influence of the new metro on the settlement of the existing metro. Then, five different acquisition functions, that is, expected improvement (EI), expected improvement per second (EIPS), expected improvement per second plus (EIPSP), lower confidence bound (LCB), probability of improvement (PI) are selected to construct BO model, and then BO-GPBE model is established. Finally, three years settlement monitoring data were collected by structural health monitoring (SHM) system installed on Nanjing Metro Line 10 are employed to demonstrate the effectiveness of BO-GPBE for forecasting the settlement.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.905-912
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• 2012

We are dealing with the Bayes' rule education tool with Excel Macro and its usage example. When an event occurs, we are interested in whether it does under certain conditions or not. In this case, we use the Bayes' rule to calculate the probability. Bayes' rule is very useful in making decision based on newly obtained statistical information. We introduce an efficient self-teaching educational tool developed to help the learners understand the Bayes' rule through intermediate steps and descriptions. The concept and examples of intermediate steps such as conditional probability, multiplication rule, law of total probability, prior probability and posterior probability could be acquired through step-by-step learning. All the processes leading to result are given with diagrams and detailed descriptions. By just clicking the execution button, users could get the results in one screen.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.48 no.7
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• pp.537-546
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• 2020

The reliability used as a performance indicator during the development of space launch vehicle should be validated by the launch success probability, and the launch data need to be fed back for reliability management. In this paper, the launch data of space launch vehicles around the world were investigated and statistically analyzed for the success probabilities according to the launch vehicle models and maturity. The Bayesian estimation of launch success probability was reviewed and analyzed by comparing the estimated success probabilities using several prior distributions and the statistical success probability. We presented the method of generating prior distribution function and considerations for Bayesian estimation.