• Journal of the Korean Data and Information Science Society
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• 제23권2호
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• pp.299-307
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• 2012

데이터마이닝은 주어진 데이터베이스에서 항목간의 흥미로운 관계를 찾아내는 기법으로서 의사결정나무는 데이터마이닝의 대표적인 알고리즘이라고 할 수 있다. 의사결정나무는 관심대상이 되는 집단을 몇 개의 소집단으로 분류하거나 예측을 수행하는 방법이다. 일반적으로 연구자가 의사결정나무 모형을 생성 할 때 모형 생성의 기준 및 입력 변수의 수에 따라 복잡한 모형이 생성되기도 한다. 특히 의사결정나무 모형에서 입력 변수의 수가 많을 경우 생성된 모형은 복잡한 형태가 될 수 있고, 모형 분석이 어려울 수도 있다. 만일 입력변수에서 주변조건부 변수 (매개변수, 외적변수)가 존재한다면 이 입력변수는 직접적인 관련성이 없는 것으로 판단한다. 이에 본 논문에서는 주변조건부 변수를 고려하여 의사결정나무모형을 생성하는 방법을 제시하고 그 효율성을 파악하기 위하여 실제 자료에 적용하고자 한다.

• 전자공학회논문지B
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• 제28B권9호
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• pp.692-703
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• 1991

A Korean word is composed of syllables. A Korean syllable is regarded as a random variable according to its probabilistic property in occurrence. A Korean syllable is divided into 'choseong', 'jungseong', and 'jongseong' which are regarded as random variables. We can consider teh conditional probatility of syllable as an index which represents the occurrence correlation between syllables in Korean words. Since the number of syllables is enormous, we use the conditional probability of a' choseong', a 'jungseong', and a 'jongseong' between syllables as an index which represents the occurrence correlation between syllables in Korean words. The length distribution of Korean woeds is computed according to frequency and to kind. Form the cumulative frequency of a Korean syllable computed from multi-syllable Korean woeds, all probabilities and conditiona probabilities are computed for the three random variables. The conditional probabilities of 'choseong'- 'choseong', 'jungseong'- 'jungseong', 'jongseong'-'jongseong', 'jongseong'-'choseong' between adjacent syllables in multi-syllable Korean woeds are computed.

• Korean Journal of Mathematics
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• 제24권3호
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• pp.573-586
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• 2016

We study the conditional integral transforms and conditional convolutions of functionals defined on K[0, T]. We consider a general vector-valued conditioning functions $X_k(x)=({\gamma}_1(x),{\ldots},{\gamma}_k(x))$ where ${\gamma}_j(x)$ are Gaussian random variables on the Wiener space which need not depend upon the values of x at only finitely many points in (0, T]. We then obtain several relationships and formulas for the conditioning functions that exist among conditional integral transform, conditional convolution and first variation of functionals in $E_{\sigma}$.

• 한국연소학회:학술대회논문집
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• 한국연소학회 1997년도 제15회 KOSCO SYMPOSIUM 논문집
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• pp.113-122
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• 1997

The conditional moment closure formulation considering the molecular and turbulent diffusion is derived. A simplified solution procedure is proposed to reduce the computational burden due to the increased dimensionality of the conditionally averaged variables. A conditionally averaged variable is expressed as a linear weighted average of the two extremes, 'no reaction' and 'equilibrium' states. The modified elliptic-type conditional moment closure formulation is implemented to simulate a two dimensional nonpremixed mixing layer reacting flow. Results show good agreement for the conditional averages of the species concentration in Bilger et al.

• Journal of applied mathematics & informatics
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• 제30권3_4호
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• pp.623-633
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• 2012

Let {${\Omega}$, $\mathcal{F}$, P} be a probability space and {$X_n|n{\geq}1$} be a sequence of random variables defined on it. A finite sequence of random variables {$X_n|n{\geq}1$} is said to be conditionally negatively associated given $\mathcal{F}$ if for every pair of disjoint subsets A and B of {1, 2, ${\cdots}$, n}, $Cov^{\mathcal{F}}(f_1(X_i,i{\in}A),\;f_2(X_j,j{\in}B)){\leq}0$ a.s. whenever $f_1$ and $f_2$ are coordinatewise nondecreasing functions. We extend the H$\grave{a}$jek-R$\grave{e}$nyi-type inequality from negative association to conditional negative association of random variables. In addition, some corollaries are given.

• 충청수학회지
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• 제27권2호
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• pp.243-247
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• 2014

Let {$X_n$, $n{\geq}1$} be a sequence of i.i.d. random variables with absolutely continuous cumulative distribution function(cdf) F(x) and the corresponding probability density function(pdf) f(x). In this paper, we give characterizations of Pareto and Weibull distribution by considering conditional expectations of record values.

• 충청수학회지
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• 제22권2호
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• pp.149-153
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• 2009

Let {$X_{n},\;n\;\geq\;1$} be a sequence of independent and identically distributed random variables with absolutely continuous cumulative distribution function (cdf) F(x) and probability density function (pdf) f(x). Suppose $X_{U(m)},\;m = 1,\;2,\;{\cdots}$ be the upper record values of {$X_{n},\;n\;\geq\;1$}. It is shown that the linearity of the conditional expectation of $X_{U(n+2)}$ given $X_{U(n)}$ characterizes the lomax, exponential and pareto distributions.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.405-411
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• 2004

In the analysis of cancer data, it is important to make inferences of survival function and to assess the effects of covariates. Cox's proportional hazard model(PHM) and Beran's nonparametric method are generally used to estimate the survival function with covariates. We adjusted the incomplete survival time using the Buckley and James's(1979) pseudo random variables, and then proposed the estimator for the conditional survival function. Also, we carried out the simulation studies to compare the performances of the proposed method.

• Journal of the Korean Statistical Society
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• 제24권2호
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• pp.563-568
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• 1995

It is well-known that the sample mean and the sample variance are jointly sufficient under normality assumption. In this paper a proof of the joint sufficiency is given without using the factorization criterion. It is related to a finite Sanov-type conditional theorem, i.e., the conditional probability density of $Y_1$ given sample mean $\mu$ and sample variance $\sigma^2$, where $Y_1, Y_2, \cdots, Y_n$ are independently and identically distributed (i.i.d.) normal random variables with mean m and variance $\delta^2$, equals that of $Y_1$ given sample mean $\mu$ and sample variance $\sigma^2$, where $Y_1, Y_2, \cdots, Y_n$ are i.i.d. normal random variables with mean $\mu$ and variance $\sigma^2$.

• Journal of the Korean Data and Information Science Society
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• 제23권1호
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• pp.121-129
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• 2012

연관성규칙은 대용량 데이터베이스에서 각 항목들 간의 관련성을 찾아내는 기법이다. 현재 연관성규칙의 효율성을 개선하기 위하여 많은 연구자들에 의하여 제약 기반 연관성규칙의 연구가 활발하게 진행되고 있다. 연관성규칙 생성 시, 종종 많은 규칙들을 발견할 수 있다. 이들 규칙 중에서 변수들 간에 우연히 관련성이 높게 나타나는 경우가 존재할 수 있고 주변 조건부 변수 (매개변수, 외적변수)에 의하여 직접적인 관련성이 없는 규칙을 발견할 수도 있으며, 그 규칙은 간접적 해석만 가능하므로 의미가 없는 것으로 판단 할 수 있다. 이에 본 연구에서는 연관성 규칙에서 주변 조건부 변수를 고려한 연관성 규칙 생성에 관하여 연구하고자 하며, 이를 실례를 통하여 고찰하였다. 본 연구의 결과를 적용함으로써 연관성 규칙에서 의미 없는 규칙을 찾을 수 있으며, 변수들 간의 관련성을 보다 정확하고 명확하게 이해할 수 있을 것이다.