• Title/Summary/Keyword: Probability Inferences

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Identification of Implementation Strategy by Practical Interpretations of Significance Level, Significance Probability, and Known Parameters in Statistical Inferences (통계적 추론에서 유의수준, 유의확률과 모수기지의 실무적 해석에 의한 적용방안)

  • Choe, Seong-Un
    • Proceedings of the Safety Management and Science Conference
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    • 2012.04a
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    • pp.75-80
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    • 2012
  • The research presents a guideline for quality practitioners to provide a full comprehension of differences in theoretical and practical interpretations of assumed sampling errors of and significance probability of calculated p-value. Besides, the study recommends the use of statistical inferences methods with known parameters to identify the improvement effects. In practice, the quality practitioners obtain the known parameters through systematic quality Database (DB) activities.

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Copula-based common cause failure models with Bayesian inferences

  • Jin, Kyungho;Son, Kibeom;Heo, Gyunyoung
    • Nuclear Engineering and Technology
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    • v.53 no.2
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    • pp.357-367
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    • 2021
  • In general, common cause failures (CCFs) have been modeled with the assumption that components within the same group are symmetric. This assumption reduces the number of parameters required for the CCF probability estimation and allows us to use a parametric model, such as the alpha factor model. Although there are various asymmetric conditions in nuclear power plants (NPPs) to be addressed, the traditional CCF models are limited to symmetric conditions. Therefore, this paper proposes the copulabased CCF model to deal with asymmetric as well as symmetric CCFs. Once a joint distribution between the components is constructed using copulas, the proposed model is able to provide the probability of common cause basic events (CCBEs) by formulating a system of equations without symmetry assumptions. In addition, Bayesian inferences for the parameters of the marginal and copula distributions are introduced and Markov Chain Monte Carlo (MCMC) algorithms are employed to sample from the posterior distribution. Three example cases using simulated data, including asymmetry conditions in total failure probabilities and/or dependencies, are illustrated. Consequently, the copula-based CCF model provides appropriate estimates of CCFs for asymmetric conditions. This paper also discusses the limitations and notes on the proposed method.

Young Chilldren's Causal Reasoning on Psychology and Biology : Focusing on the Interaction between Domain-specificty and Domain-generality (심리와 생물 영역에서의 유아의 인과추론 : 영역특정성과 영역일반성의 상호작용)

  • Kim, Ji-Hyun
    • Journal of Families and Better Life
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    • v.26 no.5
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    • pp.333-354
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    • 2008
  • This study aimed to investigate the role of domain-specific causal mechanism information and domain-general conditional probability in young children's causal reasoning on psychology and biology. Participants were 121 3-year-olds and 121 4-year-olds recruited from seven childcare centers in Seoul, Kyonggi Province, and Busan. After participants watched moving pictures on psychological and biological phenomena, they were asked to choose appropriate cause and justify their choices. Results of this study were as follows: First, young children made different inferences according to domain-specific causal mechanisms. Second, the developmental level of causal mechanisms has a gap between psychology and biology, and biological knowledge was proved to be separate from psychological knowledge during the preschool period. Third, young children's causal reasoning was different depending on the interaction effect of domain-specific mechanisms and domain-general conditional probability: children could make more inferences based on domain-specific causal mechanisms if conditional probability between domain-appropriate cause and effect was evident. To conclude, it can be inferred that the role of domain-specific causal mechanisms and domain-general conditional probability is not competitive but complementary in young children's causal reasoning.

베이즈와 이산형 모형을 이용한 비율에 대한 추론 교수법의 고찰

  • 박태룡
    • Journal for History of Mathematics
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    • v.13 no.1
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    • pp.99-112
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    • 2000
  • In this paper we discuss the teaching methods about statistical inferences. Bayesian methods have the attractive feature that statistical conclusions can be stated using the language of subjective probability. Simple methods of teaching Bayes' rule described, and these methods are illustrated for inference and prediction problems for one proportions. Also, we discuss the advantages and disadvantages of traditional and Bayesian approachs in teaching inference.

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The Role of Domain-specific Causal Mechanism and Domain-general Conditional Probability in Young Children's Causal Reasoning on Physics and Psychology (영역특정론과 영역일반론에 따른 유아의 인과추론 - 물리, 심리 영역을 중심으로 -)

  • Kim, Jihyun;Yi, Soon Hyung
    • Korean Journal of Child Studies
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    • v.29 no.5
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    • pp.243-269
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    • 2008
  • The role of domain-specific causal mechanism information and domain-general conditional probability in young children's causal reasoning on physics and psychology was investigated with the participation of 121 3-year-olds and 121 4-year-olds recruited from seven child care centers in Seoul, Kyonggi Province, and Busan. Children watched moving pictures on physical and psychological phenomena, and were asked to choose an appropriate cause and justify their choice. Results showed that young children's causal reasoning differed depending on domain-specific mechanism. In addition, their causal reasoning on physics and psychology differed by the developmental level of causal mechanism. The interaction of domain-specific mechanism and domain-general conditional probability influenced children's causal reasoning : evident conditional probability between domain-appropriate cause and effect helped children make more inferences based on domain-specific causal mechanism.

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Noninformative priors for the ratio of parameters of two Maxwell distributions

  • Kang, Sang Gil;Kim, Dal Ho;Lee, Woo Dong
    • Journal of the Korean Data and Information Science Society
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    • v.24 no.3
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    • pp.643-650
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    • 2013
  • We develop noninformative priors for a ratio of parameters of two Maxwell distributions which is used to check the equality of two Maxwell distributions. Specially, we focus on developing probability matching priors and Je reys' prior for objectiv Bayesian inferences. The probability matching priors, under which the probability of the Bayesian credible interval matches the frequentist probability asymptotically, are developed. The posterior propriety under the developed priors will be shown. Some simulations are performed for identifying the usefulness of proposed priors in objective Bayesian inference.

A Study on Analysis of Likelihood Principle and its Educational Implications (우도원리에 대한 분석과 그에 따른 교육적 시사점에 대한 연구)

  • Park, Sun Yong;Yoon, Hyoung Seok
    • The Mathematical Education
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    • v.55 no.2
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    • pp.193-208
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    • 2016
  • This study analyzes the likelihood principle and elicits an educational implication. As a result of analysis, this study shows that Frequentist and Bayesian interpret the principle differently by assigning different role to that principle from each other. While frequentist regards the principle as 'the principle forming a basis for statistical inference using the likelihood ratio' through considering the likelihood as a direct tool for statistical inference, Bayesian looks upon the principle as 'the principle providing a basis for statistical inference using the posterior probability' by looking at the likelihood as a means for updating. Despite this distinction between two methods of statistical inference, two statistics schools get clues to compromise in a regard of using frequency prior probability. According to this result, this study suggests the statistics education that is a help to building of students' critical eye by their comparing inferences based on likelihood and posterior probability in the learning and teaching of updating process from frequency prior probability to posterior probability.

Bayesian Conjugate Analysis for Transition Probabilities of Non-Homogeneous Markov Chain: A Survey

  • Sung, Minje
    • Communications for Statistical Applications and Methods
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    • v.21 no.2
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    • pp.135-145
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    • 2014
  • The present study surveys Bayesian modeling structure for inferences about transition probabilities of Markov chain. The motivation of the study came from the data that shows transitional behaviors of emotionally disturbed children undergoing residential treatment program. Dirichlet distribution was used as prior for the multinomial distribution. The analysis with real data was implemented in WinBUGS programming environment. The performance of the model was compared to that of alternative approaches.

Bayesian Multiple Comparison of Bivariate Exponential Populations based on Fractional Bayes Factor

  • Cho, Jang-Sik;Cho, Kil-Ho;Choi, Seung-Bae
    • Journal of the Korean Data and Information Science Society
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    • v.17 no.3
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    • pp.843-850
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    • 2006
  • In this paper, we consider the Bayesian multiple comparisons problem for K bivariate exponential populations to make inferences on the relationships among the parameters based on observations. And we suggest the Bayesian procedure based on fractional Bayes factor when noninformative priors are applied for the parameters. Also, we give a numerical examples to illustrate our procedure.

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Applications of Saddlepoint Method to Stress-Strength Model

  • Na, Jong-Hwa;Kim, Woo-Chul
    • Communications for Statistical Applications and Methods
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    • v.2 no.2
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    • pp.336-346
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    • 1995
  • In many problems concerned with statistical inferences, it will be of interest to compute tail areas rather than densities. But, it is often hard to calculate the exact tail probability. Saddlepoint approximation formula to the tail probability of a smooth function of random cector is developed by DiCiccio and Martin(1991). Applications of this method to stress-strength model are considered in this paper. To obtain the generalized p-values suggested by Tsui and Weerahandi(1989), we need to calculate complicated multiple integration. However, DiCiccio and Martin's(1991) results offer a convenient method to approximate these very accurately. For many artificial data sets, we access the accuracy of DiCiccio and Martin's by comparing the approximate value with the exact one.

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