• Journal of applied mathematics & informatics
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• 제13권1_2호
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• pp.441-446
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• 2003

Let { $X_{n}$ , n $\geq$ 1} be a sequence of independent and identically distributed random variables with a common continuous distribution function F(x) and probability density function f(x). Let $Y_{n}$ = max{ $X_1$, $X_2$, …, $X_{n}$ } for n $\geq$ 1. We say $X_{j}$ is an upper record value of { $X_{n}$ , n$\geq$1} if $Y_{j}$ > $Y_{j-1}$, j > 1. The indices at which the upper record values occur are given by the record times {u(n)}, n$\geq$1, where u(n) = min{j｜j>u(n-1), $X_{j}$ > $X_{u}$ (n-1), n$\geq$2} and u(1) = 1. We call the random variable X $\in$ Beta (1, c) if the corresponding probability cumulative function F(x) of x is of the form F(x) = 1-(1-x)$^{c}$ , c>0, 0$\leq$x$\leq$1. In this paper, we will give a characterization of the beta distribution of the first kind by considering conditional expectations of record values.s.

• Journal of the Korean Statistical Society
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• 제9권2호
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• pp.121-125
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• 1980

In this note, we establish a result which characterizes a partial ordering of probability distributions on a partially ordered space by that of conditional distributions. This result is then reduced to prove the conjecture made by Nevius, Proschan and Sethuraman (1977).

• Journal of the Korean Statistical Society
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• 제23권1호
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• pp.89-102
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• 1994

A new method for approximating the average run length (ARL) of cumulative sum (CUSUM) chart is proposed. This method uses the conditional expectation for the test statistic before the stopping time and its asymptotic conditional density function. The values obtained by this method are compared with some other methods in normal and exponential case.

• Journal of the Korean Statistical Society
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• 제25권4호
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• pp.489-497
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• 1996

Lower bounds for the full level probabilities are derived under order restrictions in weights. Discussions are made on typical isotonic cones such as linear order, simple tree order, and unimodal order cones. We also discuss applications of these results for constructing conditional likelihood ratio tests for ordered hypotheses in a contingency table. A real data set on torus mandibularis will be analyzed for illustrating the testing procedure.

• 한국시뮬레이션학회논문지
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• 제20권4호
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• pp.139-147
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• 2011

전 세계적으로 자동차의 수요와 교통정보 서비스의 활용도가 높아지고 있다. 따라서 교통정보 서비스의 종류와 데이터의 양의 증가로 인해 많은 IT 자원 인프라가 필요하다. 인프라의 감소를 위해 클라우드 컴퓨팅이 주목을 받고 있으며, 자원관리를 위해 프로비저닝 스케줄링 기법이 필요하다. 본 논문에서는 클라우드 환경에서 교통정보 서비스를 위한 조건부 확률 추론을 활용한 프로비저닝 스케줄링(PSCPI: Provisioning Scheduling with Conditional Probability Inference)을 제안한다. PSCPI는 가상머신의 상태에 따라 나이브 베이즈 추론 기법을 사용하여 가상머신의 가용율에 따라 작업 할당을 할 수 있다. 나이브 베이즈 기반의 조건부 확률 추론 프로비저닝 스케줄링을 활용하여 교통정보 서비스에 높은 처리율과 활용율을 보인다.

• 한국연소학회:학술대회논문집
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• 한국연소학회 2002년도 제25회 KOSCI SYMPOSIUM 논문집
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• pp.11-17
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• 2002

The first-order conditional moment closure (CMC) model is applied to $CH_4$/Air turbulent jet diffusion flames(Sandia Flame D, E and F). The flow and mixing fields are calculated by fast chemistry assumption and a beta function pdf for mixture fraction. Reacting scalar fields are calculated by elliptic CMC formulation. The results for Flame D show reasonable agreement with the measured conditional mean temperature and mass fractions of major species, although with discrepancy on the fuel rich side. The discrepancy tends to increase as the level of local extinction increases. Second-order CMC may be needed for better prediction of these near-extinction flames.

• Communications for Statistical Applications and Methods
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• 제18권6호
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• pp.799-808
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• 2011

Let {${\Omega}$, $\mathcal{F}$, P} be a probability space and {$X_n{\mid}n{\geq}1$} be a sequence of random variables defined on it. A finite sequence of random variables {$X_i{\mid}1{\leq}i{\leq}n$} is a conditional associated given $\mathcal{F}$ if for any coordinate-wise nondecreasing functions f and g defined on $R^n$, $Cov^{\mathcal{F}}$ (f($X_1$, ${\ldots}$, $X_n$), g($X_1$, ${\ldots}$, $X_n$)) ${\geq}$ 0 a.s. whenever the conditional covariance exists. We obtain the H$\grave{a}$jek-R$\grave{e}$nyi-type inequality for conditional associated random variables. In addition, we establish the strong law of large numbers, the three series theorem, integrability of supremum, and a strong growth rate for $\mathcal{F}$-associated random variables.

• 로봇학회논문지
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• 제4권1호
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• pp.56-61
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• 2009

A probability-based data generation is a typical context-generation method that is a not only simple and strong data generation method but also easy to update generation conditions. However, the probability-based context-generation method has been found its natural-born ambiguousness and confliction problems in generated context data. In order to compensate for the disadvantages of the probabilistic random data generation method, a situation propagation network is proposed in this paper. The situation propagating network is designed to update parameters of probability functions are included in probability-based data generation model. The proposed probability-based context-generation model generates two kinds of contexts: one is related to independent contexts, and the other is related to conditional contexts. The results of the proposed model are compared with the results of the probabilitybased model with respect to performance, reduction of ambiguity, and confliction.

• Genomics & Informatics
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• 제20권2호
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• pp.17.1-17.11
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• 2022

Genetic associations have been quantified using a number of statistical measures. Entropy-based mutual information may be one of the more direct ways of estimating the association, in the sense that it does not depend on the parametrization. For this purpose, both the entropy and conditional entropy of the phenotype distribution should be obtained. Quantitative traits, however, do not usually allow an exact evaluation of entropy. The estimation of entropy needs a probability density function, which can be approximated by kernel density estimation. We have investigated the proper sequence of procedures for combining the kernel density estimation and entropy estimation with a probability density function in order to calculate mutual information. Genotypes and their interactions were constructed to set the conditions for conditional entropy. Extensive simulation data created using three types of generating functions were analyzed using two different kernels as well as two types of multifactor dimensionality reduction and another probability density approximation method called m-spacing. The statistical power in terms of correct detection rates was compared. Using kernels was found to be most useful when the trait distributions were more complex than simple normal or gamma distributions. A full-scale genomic dataset was explored to identify associations using the 2-h oral glucose tolerance test results and γ-glutamyl transpeptidase levels as phenotypes. Clearly distinguishable single-nucleotide polymorphisms (SNPs) and interacting SNP pairs associated with these phenotypes were found and listed with empirical p-values.

• 한국지능시스템학회논문지
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• 제18권4호
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• pp.512-517
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• 2008

사회고령화, 장애인구 증가는 장애인을 위해 특화된 서비스를 제공하기 위한 유비쿼터스 컴퓨팅 관련기술의 개발이 필요함을 나타낸다. 이를 위해 기존의 일방적인 관계가 아닌 사용자와 유비쿼터스 환경간의 상호작용이 지원되는 상황인식 및 서비스 추론 기술의 개발이 필요하다. 기존의 상황인식과 관련 연구는 불확실한 실세계를 도메인으로 하기 때문에 전문가 시스템을 바탕으로 베이지안 네트워크(이하, BN)와 같은 확률 기반 표현 모델을 통해 주어진 상황을 인식하였다. 본 논문에서는 다변화하는 환경과 사용자나 개발자의 개입을 최소화한 상태에서의 상황인식을 고려하여 장애활동보조 서비스 어플리케이션 도메인을 정의하고 온톨로지를 기반으로 상황정보 모델을 정의한다. 결정된 상황정보모델을 이용해 BN의 구조학습을 적용한 후 응용서비스 개발의 차원에서 장애인을 위한 서비스, Activity를 결정한다. 최종적으로 BN의 Conditional Probability Table를 적절하게 정의한 후 주어지는 임의의 상황에서의 사용자의 Activity와 Service 상태변수 값을 확률 값을 표현함으로써 상황인식의 결과를 도출한다.