• 제목/요약/키워드: Bayesian hypotheses

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A Bayesian Approach to Fuzzy Hypotheses Testing with Revision of possibility distribution

  • 강만기
    • 한국전산응용수학회:학술대회논문집
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    • 한국전산응용수학회 2003년도 KSCAM 학술발표회 프로그램 및 초록집
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    • pp.13.2-13
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    • 2003
  • We propose some properties of Bayesian fuzzy hypotheses testing by revision for prior possibility distribution and posterior possibility distribution using weighted fuzzy hypotheses versus on with loss function.

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손실함수에 의한 베이지안 퍼지 가설검정 (A Bayesian Fuzzy Hypotheses Testing with Loss Function)

  • 강만기;한성일;최규탁
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2003년도 추계 학술대회 학술발표 논문집
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    • pp.45-48
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    • 2003
  • We propose some properties of Bayesian fuzzy hypotheses testing by revision for prior possibility distribution and posterior possibility distribution using weighted fuzzy hypotheses H$\sub$0/($\theta$) versus H$_1$($\theta$) on $\theta$ with loss function.

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실현성분포의 개정에 의한 베이지안 퍼지 가설 검정 (Bayesian an Fuzzy Hypotheses by Revision of Possibility Distribution)

  • Kang, Man-Ki;Lee, Chang-Eun;Park, Kue-Tak
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2001년도 추계학술대회 학술발표 논문집
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    • pp.349-352
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    • 2001
  • We propose one properties of Bayesian fuzzy hypotheses testing by revision for prior possibility distribution and posterior possibility distribution using weighted fuzzy hypotheses H$\sub$0/($\theta$) versus H$_1$($\theta$) on $\theta$.

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와이블 수명자료들에 대한 베이지안 가설검정 (Bayesian Hypotheses Testing for the Weibull Lifetime Data)

  • 강상길;김달호;조장식
    • 품질경영학회지
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    • 제28권3호
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    • pp.1-10
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    • 2000
  • In this paper, we address the Bayesian hypotheses testing for the comparison of Weibull distributions. 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 for the comparison of Weibull lifetime model and we use these results to analyze real data sets.

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Bayesian Testing for Independence in Bivariate Exponential Model

  • Cho, Jang-Sik
    • Journal of the Korean Data and Information Science Society
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    • 제17권2호
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    • pp.521-527
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    • 2006
  • In this paper, we consider the Bayesian hypotheses testing for independence in bivariate exponential model. 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 fractional Bayes factor. Also we give some numerical results to illustrate our results.

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Bayesian Approach for Independence Test in Bivariate Exponential Model

  • 조장식
    • 한국데이터정보과학회:학술대회논문집
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    • 한국데이터정보과학회 2006년도 PROCEEDINGS OF JOINT CONFERENCEOF KDISS AND KDAS
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    • pp.327-333
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    • 2006
  • In this paper, we consider the Bayesian hypotheses testing for independence in bivariate exponential model. 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 fractional Bayes factor. Also we give some numerical results to illustrate our results.

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퍼지 사전 모수에 관한 베이지안 가설검정 (Hypotheses testing of Bayes' theorem for fuzzy prior parameters)

  • 강만기;최규탁
    • 한국지능시스템학회:학술대회논문집
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    • 한국퍼지및지능시스템학회 2005년도 추계학술대회 학술발표 논문집 제15권 제2호
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    • pp.205-208
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    • 2005
  • We have fuzzy hypotheses testing from Bayesian statistics with ideas from fuzzy sets theory to generalize Bayesian methods both for samples of fuzzy data and for prior distributions with non-precise parameters. Appling the principle of agreement index, the posterior odds ratio in the favor of hypotheses $H_0$ is equal to product of the fuzzy odds ratio and the fuzzy likelihood ratio. If the Posterior odds ratio exceeds the grade judgement, we accept the hypothesis $H_0$ for the degree.

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Hypotheses Testing for the Shape Parameter of the Weibull Lifetime Data

  • Kang, Sang-Gil;Kim, Dal-Ho;Cho, Jang-Sik
    • 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.

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On Flexible Bayesian Test Criteria for Nested Point Null Hypotheses of Multiple Regression Coefficients

  • Jae-Hyun Kim;Hea-Jung Kim
    • Communications for Statistical Applications and Methods
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    • 제3권3호
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    • pp.205-214
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    • 1996
  • As flexible Bayesian test criteria for nested point null hypotheses of multiple regression coefficients, partial and overall Bayes factors are introduced under a class of intuitively meaningful prior. The criteria lead to a simple method for considering different prior beliefs on the subspaces that constitute a partition of the coefficient parameter space. A couple of tests are suggested based on the criteria. It is shown that they enable us to obtain pairwise comparisons of hypotheses of the partitioned subspaces. Through a Monte Carlo simulation, performance of the tests based on the criteria are compared with the usual Bayesian test (based on Bayes factor)in terms of their respective powers.

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Bayesian Tests for Independence and Symmetry in Freund's Bivariate Exponential Model

  • Cho, Jang-Sik;Kim, Dal-Ho;Kang, Sang-Gil
    • Journal of the Korean Data and Information Science Society
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    • 제10권1호
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    • pp.135-146
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    • 1999
  • In this paper, we consider the Bayesian hypotheses testing for independence and symmetry in Freund's bivariate exponential model. 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 to analyze some data sets.

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