• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1119-1128
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• 2005

The Bayes factors with improper noninformative priors are defined only up to arbitrary constants. So it is known that Bayes factors are not well defined due to this arbitrariness in Bayesian hypothesis testing and model selections. The intrinsic Bayes factor and the fractional Bayes factor have been used to overcome this problem. In this paper, we suggest a Bayesian hypothesis testing based on the intrinsic Bayes factor and the fractional Bayes factor for the comparison of two lognormal variances. Using the proposed two Bayes factors, we demonstrate our results with some examples.

• Journal of the Korean Data and Information Science Society
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• 제19권4호
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• pp.1379-1390
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• 2008

This article deals with the problem of testing the difference of quantiles in exponential distributions. We propose Bayesian hypothesis testing procedures for the difference of two quantiles under the noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be defined up to a multiplicative constant. So we propose the objective Bayesian hypothesis testing procedures based on the fractional Bayes factor and the intrinsic Bayes factor under the matching prior. Simulation study and a real data example are provided.

• Journal of the Korean Data and Information Science Society
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• 제26권1호
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• pp.261-269
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• 2015

Nowadays the application of change point analysis has been indispensable in a wide range of areas such as quality control, finance, environmetrics, medicine, geographics, and engineering. Identification of times where process changes would help minimize the consequences that might happen afterwards. The main objective of this paper is to compare the change-point detection capabilities of Bayesian estimate and maximum likelihood estimate. We applied Bayesian and maximum likelihood techniques to formulate change points having a step change and multiple number of change points in a Poisson rate. After a signal from c-chart and Poisson cumulative sum control charts have been detected, Monte Carlo simulation has been applied to investigate the performance of Bayesian and maximum likelihood estimation. Change point detection capacities of Bayesian and maximum likelihood estimation techniques have been investigated through simulation. It has been found that the Bayesian estimates outperforms standard control charts well specially when there exists a small to medium size of step change. Moreover, it performs convincingly well in comparison with the maximum like-lihood estimator and remains good choice specially in confidence interval statistical inference.

• 한국정보통신학회논문지
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• 제10권5호
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• pp.956-961
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• 2006

키스트로크 시간간격은 컴퓨터사용자의 검증 및 인식에서 분별적인 특징이 될 수 있다. 본 논문은 키스트로크 시간간격을 특징으로, 신경망의 역전파 알고리즘과 Bayesian 분류기, 그리고 k-NN을 이용한 분류기의 사용자 인식 성능을 비교 실험하였다. 실험 결과, 사용자당 샘플의 개수가 작을 경우에는 k-NN 알고리즘이 가장 성능이 좋았고, 사용자당 샘플의 개수가 많을 경우에는 Bayesian 분류기의 성능이 가장 뛰어난 결과를 보였다. 따라서 웹기반 온라인 사용자인식을 위해서는 사용자별 키스트로크 샘플의 수에 따라 k-NN이나 Bayesian 분류기를 선택적으로 사용하는 것이 바람직할 것으로 보인다.

• Journal of the Korean Data and Information Science Society
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• 제27권3호
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• pp.835-844
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• 2016

We develop the testing procedures about the homogeneity of the shape parameters of several inverse Gaussian distributions in our paper. We propose default Bayesian testing procedures for the shape parameters under the reference priors. The Bayes factor based on the proper priors gives the successful results for Bayesian hypothesis testing. For the case of the lack of information, the noninformative priors such as Jereys' prior or the reference prior can be used. Jereys' prior or the reference prior involves the undefined constants in the computation of the Bayes factors. Therefore under the reference priors, we develop the Bayesian testing procedures with the intrinsic Bayes factors and the fractional Bayes factor. Simulation study for the performance of the developed testing procedures is given, and an example for illustration is given.

• 한국지구물리탐사학회:학술대회논문집
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• 한국지구물리탐사학회 2003년도 Proceedings of the international symposium on the fusion technology
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• pp.340-343
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• 2003

This study presents a practical procedure for the Bayesian inversion of geophysical data by Markov chain Monte Carlo (MCMC) sampling and geostatistics. We have applied geostatistical techniques for the acquisition of prior model information, and then the MCMC method was adopted to infer the characteristics of the marginal distributions of model parameters. For the Bayesian inversion of dipole-dipole array resistivity data, we have used the indicator kriging and simulation techniques to generate cumulative density functions from Schlumberger array resistivity data and well logging data, and obtained prior information by cokriging and simulations from covariogram models. The indicator approach makes it possible to incorporate non-parametric information into the probabilistic density function. We have also adopted the MCMC approach, based on Gibbs sampling, to examine the characteristics of a posteriori probability density function and the marginal distribution of each parameter. This approach provides an effective way to treat Bayesian inversion of geophysical data and reduce the non-uniqueness by incorporating various prior information.

• Journal of the Korean Statistical Society
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• 제26권4호
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• pp.445-452
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• 1997

A more generalized version of the information inequality based on diffusivity which is a natural measure of dispersion for median-unbiased estimators developed by Sung et al. (1990) is presented. This non-Bayesian L$_{1}$ information inequality is free from regularity conditions and can be regarded as an analogue of the Chapman-Robbins inequality for mean-unbiased estimation. The approach given here, however, deals with a more generalized situation than that of the Chapman-Robbins inequality. We also develop a Bayesian version of the L$_{1}$ information inequality in median-unbiased estimation. This latter inequality is directly comparable to the Bayesian Cramer-Rao bound due to the van Trees inequality.

• 한국통신학회논문지
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• 제19권8호
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• pp.1446-1452
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• 1994

본 논문은 일반적으로 채택하고 있는 소자(device)의 수명분포인 와이블(Weibull) 분포를 적용하여 소자의 가속(accelerated) 수명 테스트에서 얻은 데이터, 즉 소자의 고정 시간을 이용하여 소자의 수명을 예측(prediction)하는데 필요한 보수(parameter)들을 추정 하는데 베이지안(Bayesian) 추정법을 이용하였다. 베이지안 추정법에서 모수를 추정하기 위해서는 사전정보가 있어야 하는데 본 논문에서는 사전정보 없이 현재의 정보만을 이용하여 모수를 추정하는 방법을 제안하였다. 스트레스가 온도인 경우, Arrhenius 모델을 적용하여 소자의 정상동작 상태에서의 수명을 예측 하는데 선형 추정을 하였다.

• 한국정보처리학회논문지
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• 제7권3호
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• pp.852-861
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• 2000

Software reliability growth models are used in testing stages of software development to model the error content and time intervals betwewn software failures. In this paper, using priors for the number of fault with the negative binomial distribution nd the error rate with gamma distribution, Bayesian inference and model selection method for Jelinski-Moranda and Goel-Okumoto and Schick-Wolverton models in software reliability. For model selection, we explored the sum of the relative error, Braun statistic and median variation. In Bayesian computation process, we could avoid the multiple integration by the use of Gibbs sampling, which is a kind of Markov Chain Monte Carolo method to compute the posterior distribution. Using simulated data, Bayesian inference and model selection is studied.

• 한국수자원학회논문집
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• 제47권5호
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• pp.469-482
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• 2014

본 연구에서는 지역특성(위도, 경도, 고도)과 기후학적 특성(연최대강우량)을 계층적 Bayesian 모형안에서 연계하여 공간적 분석이 가능한 지역빈도해석 모형을 개발하였다. 기존 지역빈도해석은 강수지점의 지리적/지형적 특성을 반영한 해석이 어려운 단점이 있으며, 지점을 기준으로 해석된 확률강수량을 유역면적강우량으로 변환 시 불확실성이 큰 단점이 있다. 이에 본 연구에서는 계층적 Bayesian 기법을 이용하여 지역특성 및 기후학적 특성이 고려된 Gumbel 확률분포형의 매개변수를 추정하였으며, 이들 매개변수들을 공간적으로 보간하여 한강유역내 모든 지점에 대해서 확률강수량을 추정할 수 있도록 하였다. 결과적으로 기존 L-모멘트 방법과 유사한 결과를 확인할 수 있었으며 확률강수량의 불확실성 정량화와 더불어 지리적/지형적 영향을 고려한 해석이 가능하였다.