• 제목/요약/키워드: Bayes' Theorem

검색결과 48건 처리시간 0.018초

네이만-피어슨 정리와 베이즈 규칙을 이용한 기업도산의 가능성 예측 (Application of Neyman-Pearson Theorem and Bayes' Rule to Bankruptcy Prediction)

  • 장경;권영식
    • 품질경영학회지
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    • 제22권3호
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    • pp.179-190
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    • 1994
  • Financial variables have been used in bankruptcy prediction. Despite of possible errors in prediction, most existing approaches do not consider the causal time sequence of prediction activity and bankruptcy phenomena. This paper proposes a prediction method using Neyman-Pearson Theorem and Bayes' rule. The proposed method uses posterior probability concept and determines a prediction policy with appropriate error rate.

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Bayes and Sequential Estimation in Hilbert Space Valued Stochastic Differential Equations

  • Bishwal, J.P.N.
    • Journal of the Korean Statistical Society
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    • 제28권1호
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    • pp.93-106
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    • 1999
  • In this paper we consider estimation of a real valued parameter in the drift coefficient of a Hilbert space valued Ito stochastic differential equation. First we consider observation of the corresponding diffusion in a fixed time interval [0, T] and prove the Bernstein - von Mises theorem concerning the convergence of posterior distribution of the parameter given the observation, suitably normalised and centered at the MLE, to the normal distribution as Tlongrightarrow$\infty$. As a consequence, the Bayes estimator of the drift parameter becomes asymptotically efficient and asymptotically equivalent to the MLE as Tlongrightarrow$\infty$. Next, we consider observation in a random time interval where the random time is determined by a predetermined level of precision. We show that the sequential MLE is better than the ordinary MLE in the sense that the former is unbiased, uniformly normally distributed and efficient but is latter is not so.

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Prediction Model of Final Project Cost using Multivariate Probabilistic Analysis (MPA) and Bayes' Theorem

  • Yoo, Wi Sung;Hadipriono, FAbian C.
    • 한국건설관리학회논문집
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    • 제8권5호
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    • pp.191-200
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    • 2007
  • This paper introduces a tool for predicting potential cost overrun during project execution and for quantifying the uncertainty on the expected project cost, which is occasionally changed by the unknown effects resulted from project's complications and unforeseen environments. The model proposed in this stuff is useful in diagnosing cost performance as a project progresses and in monitoring the changes of the uncertainty as indicators for a warning signal. This model is intended for the use by project managers who forecast the change of the uncertainty and its magnitude. The paper presents a mathematical approach for modifying the costs of incomplete work packages and project cost, and quantifying reduced uncertainties at a consistent confidence level as actual cost information of an ongoing project is obtained. Furthermore, this approach addresses the effects of actual informed data of completed work packages on the re-estimates of incomplete work packages and describes the impacts on the variation of the uncertainty for the expected project cost incorporating Multivariate Probabilistic Analysis (MPA) and Bayes' Theorem. For the illustration purpose, the Introduced model has employed an example construction project. The results are analyzed to demonstrate the use of the model and illustrate its capabilities.

신뢰도 데이터 합성 program의 개발 (A Development on Reliability Data Integration Program)

  • 이광원;박문희;오신규;한정민
    • 한국안전학회지
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    • 제18권4호
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    • pp.164-168
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    • 2003
  • Bayes theorem, suggested by the British Mathematician Bayes (18th century), enables the prior estimate of the probability of an event under the condition given by a specific This theorem has been frequently used to revise the failure probability of a component or system. 2-Stage Bayesian procedure was firstly published by Shultis et al. (1981) and Kaplan (1983), and was further developed based on the studies of Hora & Iman (1990) Papazpgolou et al., Porn(1993). For a small observed failure number (below 12), the estimated reliability of a system or component is not reliable. In the case in which the reliability data of the corresponding system or component can be found in a generic reliability reference book, however, a reliable estimation of the failure probability can be realized by using Bayes theorem, which jointly makes use of the observed data (specific data) and the data found in reference book (generic data).

A SIMULATION STUDY OF BAYESIAN PROPORTIONAL HAZARDS MODELS WITH THE BETA PROCESS PRIOR

  • Lee, Jae-Yong
    • Journal of the Korean Statistical Society
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    • 제34권3호
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    • pp.235-244
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    • 2005
  • In recent years, theoretical properties of Bayesian nonparametric survival models have been studied and the conclusion is that although there are pathological cases the popular prior processes have the desired asymptotic properties, namely, the posterior consistency and the Bernstein-von Mises theorem. In this study, through a simulation experiment, we study the finite sample properties of the Bayes estimator and compare it with the frequentist estimators. To our surprise, we conclude that in most situations except that the prior is highly concentrated at the true parameter value, the Bayes estimator performs worse than the frequentist estimators.

Admissibility of Some Stepwise Bayes Estimators

  • Kim, Byung-Hwee
    • Journal of the Korean Statistical Society
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    • 제16권2호
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    • pp.102-112
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    • 1987
  • This paper treats the problem of estimating an arbitrary parametric function in the case when the parameter and sample spaces are countable and the decision space is arbitrary. Using the notions of a stepwise Bayesian procedure and finite admissibility, a theorem is proved. It shows that under some assumptions, every finitely admissible estimator is unique stepwise Bayes with respect to a finite or countable sequence of mutually orthogonal priors with finite supports. Under an additional assumption, it is shown that the converse is true as well. The first can be also extended to the case when the parameter and sample space are arbitrary, i.e., not necessarily countable, and the underlying probability distributions are discrete.

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베이지안 기법의 발전 및 수자원 분야에의 적용 (Development of the Bayesian method and its application to the water resources field)

  • 나우영;유철상
    • 한국수자원학회논문집
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    • 제54권1호
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    • pp.1-13
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    • 2021
  • 베이지안 기법은 수자원을 포함한 다양한 분야에서 매우 유용한 통계적 도구로 이용되고 있다. 이에 본 연구에서는 베이지안 통계학에 대해 그 배경을 고찰하고, 수자원 분야에 적용된 사례를 소개하였다. 먼저, 베이지안 통계학의 탄생에서부터 현재에 이르기까지의 발전 과정과 이에 기여한 베이지안 통계학자들의 업적 등을 정리하였다. 다음으로 베이지안 기법의 근간이 되는 베이즈 정리의 유도 과정을 제시하고, 베이즈 정리의 세 요소인 사전분포, 우도함수, 사후분포의 역할에 대해 설명하였다. 또한, 베이지안 통계학이 가지는 고유한 특징과 장점에 대해 정리하였다. 마지막으로 수자원 분야에 베이지안 기법이 적용된 사례를 여러 범주로 나누어 정리하였다. 베이지안 기법은 정보 및 빅데이터의 활용이 커짐에 따라 수자원 분야에서 더욱 유용하게 적용될 것으로 전망된다.