• Title/Summary/Keyword: Bayesian

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Recent advances in Bayesian inference of isolation-with-migration models

  • Chung, Yujin
    • Genomics & Informatics
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    • v.17 no.4
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    • pp.37.1-37.8
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    • 2019
  • Isolation-with-migration (IM) models have become popular for explaining population divergence in the presence of migrations. Bayesian methods are commonly used to estimate IM models, but they are limited to small data analysis or simple model inference. Recently three methods, IMa3, MIST, and AIM, resolved these limitations. Here, we describe the major problems addressed by these three software and compare differences among their inference methods, despite their use of the same standard likelihood function.

Bayesian Test for Equality of Coefficients of Variation in the Normal Distributions

  • Lee, Hee-Choon;Kang, Sang-Gil;Kim, Dal-Ho
    • 한국데이터정보과학회:학술대회논문집
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    • 2003.10a
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    • pp.49-56
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    • 2003
  • When X and Y have independent normal distributions, we develop a Bayesian testing procedure for the equality of two coefficients of variation. Under the reference prior of the coefficient of variation, we propose a Bayesian test procedure for the equality of two coefficients of variation using fractional Bayes factor. A real data example is provided.

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Bayesian Prediction Analysis for the Exponential Model Under the Censored Sample with Incomplete Information

  • Kim, Yeung-Hoon;Ko, Jeong-Hwan
    • Journal of the Korean Data and Information Science Society
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    • v.13 no.1
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    • pp.139-145
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    • 2002
  • This paper deals with the problem of obtaining the Bayesian predictive density function and the prediction intervals for a future observation and the p-th order statistics of n future observations for the exponential model under the censored sampling with incomplete information.

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A Closed-Form Bayesian Inferences for Multinomial Randomized Response Model

  • Heo, Tae-Young;Kim, Jong-Min
    • Communications for Statistical Applications and Methods
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    • v.14 no.1
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    • pp.121-131
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    • 2007
  • In this paper, we examine the problem of estimating the sensitive characteristics and behaviors in a multinomial randomized response model using Bayesian approach. We derived a posterior distribution for parameter of interest for multinomial randomized response model. Based on the posterior distribution, we also calculated a credible intervals and mean squared error (MSE). We finally compare the maximum likelihood estimator and the Bayes estimator in terms of MSE.

Reference-Intrinstic Analysis for the Difference between Two Normal Means

  • Jang, Eun-Jin;Kim, Dal-Ho;Lee, Kyeong-Eun
    • Communications for Statistical Applications and Methods
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    • v.14 no.1
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    • pp.11-21
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    • 2007
  • In this paper, we consider a decision-theoretic oriented, objective Bayesian inference for the difference between two normal means with unknown com-mon variance. We derive the Bayesian reference criterion as well as the intrinsic estimator and the credible region which correspond to the intrinsic discrepancy loss and the reference prior. We illustrate our results using real data analysis as well as simulation study.

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

  • 강만기;한성일;최규탁
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2003.09b
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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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Bayesian Multiple Comparisons for the Ratio of the Failure Rates in Two Components System

  • Cho, Jang-Sik;Cho, Kil-Ho
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.2
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    • pp.647-655
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    • 2006
  • In this paper, we consider multiple comparisons for the ratio of the failure rates in two components system that the lifetimes of the components have independent exponential distributions. Also we suggest Bayesian multiple comparisons procedure based on fractional Bayes factor when noninformative priors are applied for the parameters. Finally, we give numerical examples to illustrate our procedure.

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Bayesian hypothesis testing for homogeneity of coecients of variation in k Normal populationsy

  • Kang, Sang-Gil
    • Journal of the Korean Data and Information Science Society
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    • v.21 no.1
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    • pp.163-172
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    • 2010
  • In this paper, we deal with the problem for testing homogeneity of coecients of variation in several normal distributions. We propose Bayesian hypothesis testing procedures based on the Bayes factor under noninformative prior. The noninformative prior is usually improper which yields a calibration problem that makes the Bayes factor to be dened 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 reference prior. Simulation study and a real data example are provided.

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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    • v.17 no.2
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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 Hypotheses Testing for the Weibull Lifetime Data (와이블 수명자료들에 대한 베이지안 가설검정)

  • 강상길;김달호;조장식
    • Journal of Korean Society for Quality Management
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    • v.28 no.3
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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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