• 한국신뢰성학회지:신뢰성응용연구
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• 제13권1호
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• pp.1-10
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• 2013

A Bayesian zero-failure reliability demonstration test method for products with exponential lifetime distribution is presented. Beta prior distribution for reliability of a product is used to design the Bayesian test plan and selecting a prior distribution using a prior test information is discussed. A test procedure with zero-failure acceptance criterion is developed that guarantees specified reliability of a product with given confidence level. An example is provided to illustrate the use of the developed Bayesian reliability demonstration test method.

• Journal of the Korean Data and Information Science Society
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• 제25권1호
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• pp.187-194
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• 2014

We consider a Bayesian nonignorable selection model to accommodate the selection bias. Markov chain Monte Carlo methods is known to be very useful to fit the nonignorable selection model. However, sensitivity to prior assumptions on parameters for selection mechanism is a potential problem. To quantify the sensitivity to prior assumption, the deviance information criterion and the conditional predictive ordinate are used to compare the goodness-of-fit under two different prior specifications. It turns out that the 'MLE' prior gives better fit than the 'uniform' prior in viewpoints of goodness-of-fit measures.

• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2009년도 춘계학술대회 미래 IT융합기술 및 전략
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• pp.107-113
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• 2009

This paper deals with noninformative priors for such as Jeffres' prior, reference prior and probability matching prior for scale parameter of exponential distribution when the data are collected in multiple step stress accelerated life tests. We find the noninformative priors for this model and show that the reference prior satisfies first order matching criterion. Using artificial data, we perform Bayesian analysis for proposed priors.

• 품질경영학회지
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• 제27권4호
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• pp.153-166
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• 1999

In this paper, we address the Bayesian hypotheses testing for the shape parameter of weibull model. In Bayesian testing problem, conventional Bayes factors can not typically accommodate the use of noninformative priors which are improper and are defined only up to arbitrary constants. To overcome such problem, we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. We derive the arithmetic and median intrinsic Bayes factors and use these results to analyze real data sets.

• Communications for Statistical Applications and Methods
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• 제7권1호
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• pp.151-164
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• 2000

In this paper we consider the Bayesian hypothese testing for independence and symmetry in Freund's bivariate exponential model with censored data In Bayesian testing problem we use the noninformative priors for parameters which are improper and are defined only up to arbitrary constants. And we use the recently proposed hypotheses testing criterion called the intrinsic Bayes factor. Also we derive the arithmetic and median intrinsic Bayes factors and use these results of analyze some data sets.

• 응용통계연구
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• 제16권2호
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• pp.365-376
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• 2003

본 연구에서는 비정규 시계열 자료에 관한 다양한 회귀모형을 고찰하고, 이들 모형의 선택 기준에 관하여 연구해 보았다. 모형 선택의 기준으로는 AIC (Akaike information criterion), BIC (Baysian information criterion) 그리고 우도비 검정을 확장 적용하였다. 또한, 실제의 Polio 자료분석을 통해 이를 적용해보았다.

• Journal of the Korean Data and Information Science Society
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• 제17권3호
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• pp.743-752
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• 2006

This paper considers a Bayesian approach to replacement policy model with minimal repair. We use the criterion based on the expected cost and the expected downtime to determine the optimal replacement period. To do so, we obtain the expected cost rate per unit time and the expected downtime per unit time, respectively. When the failure time is Weibull distribution with uncertain parameters, a Bayesian approach is established to formally express and update the uncertain parameters for determining an optimal maintenance policy. Especially, the overall value function suggested by Jiagn and Ji(2002) is applied to obtain the optimal replacement period. The numerical examples are presented for illustrative purpose.

• Journal of the Korean Data and Information Science Society
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• 제20권3호
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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.

• 한국신뢰성학회:학술대회논문집
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• 한국신뢰성학회 2002년도 정기학술대회
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• pp.275-279
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• 2002

A Bayesian acceptance sampling plan for limited failure populations are developed. We consider a situation where defective products have short lifetimes and non-defective ones never fail during the technological life of the products. An acceptance criterion which guarantees the out going quality of accepted products is derived using the prior information on the quality of lots submitted for inspection. Numerical examples are provided.

• International Journal of Computer Science & Network Security
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• 제23권4호
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• pp.85-94
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• 2023

Image halftoning is a technique for varying grayscale images into two-tone binary images. Unfortunately, the static representation of an image-half toning, wherever each pixel intensity is combined by its local neighbors only, causes missing subjective problem. Also, the existing noise causes an instability criterion. In this paper an image half-toning is represented as a dynamical system for recognizing the global representation. Also, noise is reduced based on a probabilistic model. Since image half-toning is considered as 2-D matrix with a full connected pass, this structure is recognized by the dynamical system of Cellular Neural Networks (CNNs) which is defined by its template. Bayesian Rough Sets is used in exploiting the ideal CNNs construction that synthesis its dynamic. Also, Bayesian rough sets contribute to enhance the quality of the halftone image by removing noise and discovering the effective parameters in the CNNs template. The novelty of this method lies in finding a probabilistic based technique to discover the term of CNNs template and define new learning rules for CNNs internal work. A numerical experiment is conducted on image half-toning corrupted by Gaussian noise.