• Title/Summary/Keyword: Conditional variable

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Validation of an asymptotic zone conditional expression for turbulent burning velocity against DNS database (영역조건평균에 기초한 난류연소속도의 직접수치해법검증)

  • Kim, Soo-Youb;Huh, Kang-Y.
    • 한국연소학회:학술대회논문집
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    • 2004.11a
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    • pp.62-69
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    • 2004
  • Zone conditional formulations for the Reynolds average reaction progress variable are used to derive an asymptotic expression for turbulent burning velocity. New DNS runs are performed for validation in a statistically one dimensional steady state configuration. Parametric study is performed with respect to turbulent intensity, integral length scale, density ratio and laminar flame speed. Results show good agreement between DNS results and the asymptotic expression in terms of measured maximum flame surface density and estimated turbulent diffusivity in unburned gas.

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Validation of an asymptotic zone conditional expression for turbulent burning velocity against DNS database (영역조건평균에 기초한 난류연소속도의 직접수치해법검증)

  • Kim, Soo-Youb;Huh, Kang-Y.
    • Journal of the Korean Society of Combustion
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    • v.9 no.4
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    • pp.1-8
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    • 2004
  • Zone conditional formulation for the Reynolds average reaction progress variable is used to derive an asymptotic expression for turbulent burning velocity. New DNS runs are performed for validation in a statistically one dimensional steady state configuration. Parametric study is performed with respect to turbulent intensity, integral length scale, density ratio and laminar flame speed. Results show good agreement between DNS results and the asymptotic expression in terms of measured maximum flame surface density and estimated turbulent diffusivity in unburned gas.

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Variable Selection with Log-Density in Logistic Regression Model (로지스틱회귀모형에서 로그-밀도비를 이용한 변수의 선택)

  • Kahng, Myung-Wook;Shin, Eun-Young
    • Communications for Statistical Applications and Methods
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    • v.19 no.1
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    • pp.1-11
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    • 2012
  • We present methods to study the log-density ratio of the conditional densities of the predictors given the response variable in the logistic regression model. This allows us to select which predictors are needed and how they should be included in the model. If the conditional distributions are skewed, the distributions can be considered as gamma distributions. A simulation study shows that the linear and log terms are required in general. If the conditional distributions of xjy for the two groups overlap significantly, we need both the linear and log terms; however, only the linear or log term is needed in the model if they are well separated.

EVALUATION OF SOME CONDITIONAL WIENER INTEGRALS

  • Chang, Kun-Soo;Chang, Joo-Sup
    • Bulletin of the Korean Mathematical Society
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    • v.21 no.2
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    • pp.99-106
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    • 1984
  • J. Yeh has recently introduced the concept of conditional Wiener integrals which are meant specifically the conditional expectation E$^{w}$ (Z vertical bar X) of a real or complex valued Wiener integrable functional Z conditioned by the Wiener measurable functional X on the Wiener measure space (A precise definition of the conditional Wiener integral and a brief discussion of the Wiener measure space are given in Section 2). In [3] and [4] he derived some inversion formulae for conditional Wiener integrals and evaluated some conditional Wiener integrals E$^{w}$ (Z vertical bar X) conditioned by X(x)=x(t) for a fixed t>0 and x in Wiener space. Thus E$^{w}$ (Z vertical bar X) is a real or complex valued function on R$^{1}$. In this paper we shall be concerned with the random vector X given by X(x) = (x(s$_{1}$),..,x(s$_{n}$ )) for every x in Wiener space where 0=s$_{0}$ $_{1}$<..$_{n}$ =t. In Section 3 we will evaluate some conditional Wiener integrals E$^{w}$ (Z vertical bar X) which are real or complex valued functions on the n-dimensional Euclidean space R$^{n}$ . Thus we extend Yeh's results [4] for the random variable X given by X(x)=x(t) to the random vector X given by X(x)=(x(s$_{1}$).., x(s$_{n}$ )).

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Conditional Probability of a 'Choseong', a 'Jungseong', and a 'Jongseong' Between Syllables in Multi-Syllable Korean Words (한국어 다음절 단어의 초성, 중성, 종성단위의 음절간 조건부 확률)

  • 이재홍;이재학
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.28B no.9
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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.

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Efficient variable selection method using conditional mutual information (조건부 상호정보를 이용한 분류분석에서의 변수선택)

  • Ahn, Chi Kyung;Kim, Donguk
    • Journal of the Korean Data and Information Science Society
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    • v.25 no.5
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    • pp.1079-1094
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    • 2014
  • In this paper, we study efficient gene selection methods by using conditional mutual information. We suggest gene selection methods using conditional mutual information based on semiparametric methods utilizing multivariate normal distribution and Edgeworth approximation. We compare our suggested methods with other methods such as mutual information filter, SVM-RFE, Cai et al. (2009)'s gene selection (MIGS-original) in SVM classification. By these experiments, we show that gene selection methods using conditional mutual information based on semiparametric methods have better performance than mutual information filter. Furthermore, we show that they take far less computing time than Cai et al. (2009)'s gene selection but have similar performance.

Computing Methods for Generating Spatial Random Variable and Analyzing Bayesian Model (확률난수를 이용한 공간자료가 생성과 베이지안 분석)

  • 이윤동
    • The Korean Journal of Applied Statistics
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    • v.14 no.2
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    • pp.379-391
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    • 2001
  • 본 연구에서는 관심거리가 되고 있는 마코프인쇄 몬테칼로(Markov Chain Monte Carlo, MCMC)방법에 근거한 공간 확률난수 (spatial random variate)생성법과 깁스표본추출법(Gibbs sampling)에 의한 베이지안 분석 방법에 대한 기술적 사항들에 관하여 검토하였다. 먼저 기본적인 확률난수 생성법과 관련된 사항을 살펴보고, 다음으로 조건부명시법(conditional specification)을 이용한 공간 확률난수 생성법을 예를 들어 살펴보기로한다. 다음으로는 이렇게 생성된 공간자료를 분석하기 위하여 깁스표본추출법을 이용한 베이지안 사후분포를 구하는 방법을 살펴보았다.

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The Algorithm of Simulation and Justification of the Use of Conditional Likclihood in Up-and-Down Method

  • Hyonggi Jung
    • Communications for Statistical Applications and Methods
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    • v.4 no.1
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    • pp.271-280
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    • 1997
  • It should be noted that the number of observation in the up-and-down procedure is a random variable. Therefore, We need to justify the employment of the conditional likelihood even in the above situation and show the algorithm of simulation. Also, the strategy of halving or widening the level space in modified up-and-down method is suggested.

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Limit Theorems for Fuzzy Martingales

  • Joo, Sang-Yeol;Kim, Gwan-Young;Kim, Yun-Kyong
    • Journal of the Korean Statistical Society
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    • v.28 no.1
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    • pp.21-34
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    • 1999
  • In this paper, conditional expectation of a fuzzy random variable is introduced and its properties are investigated. Using this, we introduce the concept of fuzzy martingales and prove some convergence theorems which generalize te corresponding results for the classical martingales.

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On the maximum and minimum in a bivariate uniform distribution

  • Lee, Changsoo;Shin, Hyejung;Moon, Yeung-Gil
    • Journal of the Korean Data and Information Science Society
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    • v.26 no.6
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    • pp.1495-1500
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    • 2015
  • We obtain means and variances of max {X, Y} and min {X, Y} in the underlying Morgenstern type bivariate uniform variables X and Y with same scale parameters and different scale parameters respectively. And we obtain the conditional expectations in the underlying Morgenstern type bivariate uniform variables. Here, we shall consider the conditional expectations to know the dependence of one variable on the other variable and we consider the behaviors of means and variances of max {X, Y} and min {X, Y} with respect to changes in means, variances, and the correlation coeffcient of the underlying Morgenstern type bivariate uniform variables.