• Title/Summary/Keyword: prior distributions

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베이즈 법칙의 활용을 위한 엑셀 매크로 (Excel macro for applying Bayes' rule)

  • 김재현;백호유
    • Journal of the Korean Data and Information Science Society
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    • 제22권6호
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    • pp.1183-1197
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    • 2011
  • 베이즈 법칙에서는 사전확률과 우도가 주어지고 어떤 실험결과가 일어났을 때 사후확률을 계산한다. 이러한 사후확률의 계산 문제를 엑셀 매크로를 이용하여 쉽게 계산할 수 있다. 또한 일련의 독립적이고 연속적인 실험결과에 따르는 사후확률도 편리하게 계산할 수 있다. 특히, 엑셀 매크로를 작성하면 작업창에서 반복된 계산의 입력과 출력이 쉽게 이루어진다. 본 논문에서는 베이즈 법칙의 활용을 위해서 엑셀 매크로를 작성하고 그것의 사용 예를 들었다.

변곡 S-형 소프트웨어 신뢰도성장모형의 베이지안 모수추정 (Bayesian Estimation for Inflection S-shaped Software Reliability Growth Model)

  • 김희수;이종형;박동호
    • 품질경영학회지
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    • 제37권4호
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    • pp.16-22
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    • 2009
  • The inflection S-shaped software reliability growth model (SRGM) proposed by Ohba(1984) is one of the most commonly used models and has been discussed by many authors. The main purpose of this paper is to estimate the parameters of Ohba's SRGM within the Bayesian framework by applying the Markov chain Monte Carlo techniques. While the maximum likelihood estimates for these parameters are well known, the Bayesian method for the inflection S-shaped SRGM have not been discussed in the literature. The proposed methods can be quite flexible depending on the choice of prior distributions for the parameters of interests. We also compare the Bayesian methods with the maximum likelihood method numerically based on the real data.

Bayesian Inversion of Gravity and Resistivity Data: Detection of Lava Tunnel

  • Kwon, Byung-Doo;Oh, Seok-Hoon
    • 한국지구과학회지
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    • 제23권1호
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    • pp.15-29
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    • 2002
  • Bayesian inversion for gravity and resistivity data was performed to investigate the cavity structure appearing as a lava tunnel in Cheju Island, Korea. Dipole-dipole DC resistivity data were proposed for a prior information of gravity data and we applied the geostatistical techniques such as kriging and simulation algorithms to provide a prior model information and covariance matrix in data domain. The inverted resistivity section gave the indicator variogram modeling for each threshold and it provided spatial uncertainty to give a prior PDF by sequential indicator simulations. We also presented a more objective way to make data covariance matrix that reflects the state of the achieved field data by geostatistical technique, cross-validation. Then Gaussian approximation was adopted for the inference of characteristics of the marginal distributions of model parameters and Broyden update for simple calculation of sensitivity matrix and SVD was applied. Generally cavity investigation by geophysical exploration is difficult and success is hard to be achieved. However, this exotic multiple interpretations showed remarkable improvement and stability for interpretation when compared to data-fit alone results, and suggested the possibility of diverse application for Bayesian inversion in geophysical inverse problem.

베이지안 확률 모형을 이용한 위험률 함수의 추론 (Hazard Rate Estimation from Bayesian Approach)

  • 김현묵;안선응
    • 산업경영시스템학회지
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    • 제28권3호
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    • pp.26-35
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    • 2005
  • This paper is intended to compare the hazard rate estimations from Bayesian approach and maximum likelihood estimate(MLE) method. Hazard rate frequently involves unknown parameters and it is common that those parameters are estimated from observed data by using MLE method. Such estimated parameters are appropriate as long as there are sufficient data. Due to various reasons, however, we frequently cannot obtain sufficient data so that the result of MLE method may be unreliable. In order to resolve such a problem we need to rely on the judgement about the unknown parameters. We do this by adopting the Bayesian approach. The first one is to use a predictive distribution and the second one is a method called Bayesian estimate. In addition, in the Bayesian approach, the prior distribution has a critical effect on the result of analysis, so we introduce the method using computerized-simulation to elicit an effective prior distribution. For the simplicity, we use exponential and gamma distributions as a likelihood distribution and its natural conjugate prior distribution, respectively. Finally, numerical examples are given to illustrate the potential benefits of the Bayesian approach.

Local Sensitivity Analysis using Divergence Measures under Weighted Distribution

  • Chung, Younshik;Dey, Dipak K.
    • Journal of the Korean Statistical Society
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    • 제30권3호
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    • pp.467-480
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    • 2001
  • This paper considers the use of local $\phi$-divergence measures between posterior distributions under classes of perturbations in order to investigate the inherent robustness of certain classes. The smaller value of the limiting local $\phi$-divergence implies more robustness for the prior or the likelihood. We consider the cases when the likelihood comes form the class of weighted distribution. Two kinds of perturbations are considered for the local sensitivity analysis. In addition, some numerical examples are considered which provide measures of robustness.

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Bayesian One-Sided Testing for the Ratio of Poisson Means

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • Journal of the Korean Data and Information Science Society
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    • 제17권2호
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    • pp.619-631
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    • 2006
  • When X and Y have independent Poisson distributions, we develop a Bayesian one-sided testing procedures for the ratio of two Poisson means. We propose the objective Bayesian one-sided testing procedures for the ratio of two Poisson means based on the fractional Bayes factor and the intrinsic Bayes factor. Some real examples are provided.

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Bayesian Analysis under Heavy-Tailed Priors in Finite Population Sampling

  • Kim, Dal-Ho;Lee, In-Suk;Sohn, Joong-Kweon;Cho, Jang-Sik
    • Communications for Statistical Applications and Methods
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    • 제3권3호
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    • pp.225-233
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    • 1996
  • In this paper, we propose Bayes estimators of the finite population mean based on heavy-tailed prior distributions using scale mixtures of normals. Also, the asymptotic optimality property of the proposed Bayes estimators is proved. A numerical example is provided to illustrate the results.

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A Study on the Posterior Density under the Bayes-empirical Bayes Models

  • Sohn, Joong-K.Sohn;Kim, Heon-Joo-Kim
    • Communications for Statistical Applications and Methods
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    • 제3권3호
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    • pp.215-223
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    • 1996
  • By using Tukey's generalized lambda distribution, appoximate posterior density is derived under the Bayes-empirical Bayes model. The sensitivity of posterior distribution to the hyperprior distribution is examined by using Tukey's generalized lambda distriburion which approximate many well-knmown distributions. Based upon Monte Varlo simulation studies it can be said that posterior distribution is sensitive to the cariance of the prior distribution and to the symmetry of the hyperprior distribution. Also posterior distribution is approximately obtained by using the following methods : Lindley method, Laplace method and Gibbs sampler method.

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Bayesian One-Sided Testing for the Ratio of Poisson Means

  • 강상길;김달호;이우동
    • 한국데이터정보과학회:학술대회논문집
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    • 한국데이터정보과학회 2006년도 PROCEEDINGS OF JOINT CONFERENCEOF KDISS AND KDAS
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    • pp.295-306
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    • 2006
  • When X and Y have independent Poisson distributions, we develop a Bayesian one-sided testing procedures for the ratio of two Poisson means. We propose the objective Bayesian one-sided testing procedures for the ratio of two Poisson means based on the fractional Bayes factor and the intrinsic Bayes factor. Some real examples are provided.

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A Bayesian Multiple Testing of Detecting Differentially Expressed Genes in Two-sample Comparison Problem

  • Oh Hyun-Sook;Yang Wan-Youn
    • Communications for Statistical Applications and Methods
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    • 제13권1호
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    • pp.39-47
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    • 2006
  • The Bayesian approach to multiple testing procedure for one sample testing problem proposed by Scott and Berger (2003) is extended to two-sample comparison problem in microarray experiments. The prior distribution of each gene's mean for one sample is given conditionally on the corresponding gene's mean for the other sample. Posterior distributions of interesting parameters are derived and estimated based on an importance sampling method. A simulated example is given for illustration.