• 한국컴퓨터정보학회논문지
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• 제23권6호
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• pp.1-7
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• 2018

A multi-label classification is to find multiple labels associated with the input pattern. Multi-label classification can be achieved by extending conventional single-label classification. Common extension techniques are known as Binary relevance, Label powerset, and Classifier chains. However, most of the extended multi-label naive bayes classifier has not been able to accurately estimate posterior probabilities because it does not reflect the label dependency. And the remaining extended multi-label naive bayes classifier has a problem that it is unstable to estimate posterior probability according to the label selection order. To estimate posterior probability well, we propose a new posterior probability estimation method that reflects the probability between all labels and labels efficiently. The proposed method reflects the correlation between labels. And we have confirmed through experiments that the extended multi-label naive bayes classifier using the proposed method has higher accuracy then the existing multi-label naive bayes classifiers.

• 대한전기학회논문지
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• 제45권2호
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• pp.325-328
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• 1996

In this paper, we proposed an estimation method of a posterior probability and PDF(Probability density function) using a feed forward neural network and code books of VQ(vector quantization). In this study, We estimates a posterior probability and probability density function, which compose a new parameter with well-known Mel cepstrum and verificate the performance for the five vowels taking from syllables by NN(neural network) and PNN(probabilistic neural network). In case of new parameter, showed the best result by probabilistic neural network and recognition rates are average 83.02%.

• Communications for Statistical Applications and Methods
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• 제12권2호
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• pp.395-411
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• 2005

In this paper, we develop the Jeffreys' prior, reference prior and the the probability matching priors for the difference of intraclass correlation coefficients in familial data. e prove the sufficient condition for propriety of posterior distributions. Using marginal posterior distributions under those noninformative priors, we compare posterior quantiles and frequentist coverage probability.

• Journal of the Korean Data and Information Science Society
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• 제13권2호
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• pp.317-328
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• 2002

We first derive group ordering reference priors in a two-way mixed-effects analysis of variance (ANOVA) model. We show that posterior distributions are proper and provide marginal posterior distributions under reference priors. We also examine whether the reference priors satisfy the probability matching criterion. Finally, the reference prior satisfying the probability matching criterion is shown to be good in the sense of frequentist coverage probability of the posterior quantile.

• 디지털융복합연구
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• 제12권12호
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• pp.293-301
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• 2014

본 논문에서는 데이터 마이닝에 필요한 클러스터링과정에서 불필요한 정보를 감축하기 위하여 베이지언 사후확률의 신뢰도를 이용한 새로운 척도를 제안한다. 데이터 감축을 위한 속성의 중요도가 클러스터링의 결과에 지배적이기 때문에 많은 속성의 변별력을 향상시키기 위하여 사후확률의 신뢰도에 정보 엔트로피를 적용하였다. 제안된 사후확률을 기반으로 한 러프 엔트로피 척도에 의한 속성의 신뢰도의 중복성은 엔트로피의 자연로그에 의하여 상당히 줄어든다. 따라서 제안된 척도에 의하여 생성된 군집화 알고리즘은 속성값의 변별력을 향상시켜 기존의 리덕트를 최소화하였고, 이는 분할의 효율성을 향상시킬 수 있었다. 제안된 알고리즘의 검증을 위해 패턴분류 문제에 적용되는 ACME 데이터에 대하여 속성간의 변별력, 분할결과에 따른 분할의 순정도를 기존의 알고리즘과 비교 분석하였다.

• 한국컴퓨터정보학회논문지
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• 제26권6호
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• pp.29-35
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• 2021

본 논문에서는 위치 측위의 정확도를 높일 수 있는 방안으로 KNN(K-Nearest Neighbor)과 Local Map Classification 및 Bayes Filter를 융합한 기법을 제안한다. 먼저 이 기법은 Local Map Classification이 실제 지도를 여러 개의 Cluster로 나누고, 다음으로 KNN으로 Cluster들을 분류한다. 그리고 Bayes Filter가 획득한 각 Cluster의 확률을 통하여 Posterior Probability을 계산한다. 이 Posterior Probability으로 로봇이 위치한 Cluster를 검색한다. 성능 평가를 위하여 KNN과 Local Map Classification 및 Bayes Filter을 적용하여서 얻은 위치 측위의 결과를 분석하였다. 분석 결과로 RSSI 신호가 변하더라도 위치 정보는 한 Cluster에 고정되면서 위치 측위의 정확도가 높아진다는 사실을 확인하였다.

• 품질경영학회지
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• 제13권1호
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• pp.20-25
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• 1985

There are several approaches for dealing with the structural change in regression model, but by introducing a concept of Spline, the structural change can be expressed more clearly. This makes it possible not only to know the location where the structural change happens and the total number, but also to derive posterior distribution from anterior-posterior distribution when the probability of the judgement anterior for entire combination was given to each model, by which, the model that has the highest posterior probability is the method which realizes the structural change. The purpose of this study is to find a peculiarity of the posterior probability on the occasion of anterior information acquired and of not acquired with Baysian approach.

• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.1-9
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• 2003

In this article, we consider a Bayesian estimation method for the geometric mean of $textsc{k}$ exponential parameters, Using the Tibshirani's orthogonal parameterization, we suggest an invariant prior distribution of the $textsc{k}$ parameters. It is seen that the prior, probability matching prior, is better than the uniform prior in the sense of correct frequentist coverage probability of the posterior quantile. Then a weighted Monte Carlo method is developed to approximate the posterior distribution of the mean. The method is easily implemented and provides posterior mean and HPD(Highest Posterior Density) interval for the geometric mean. A simulation study is given to illustrates the efficiency of the method.

• Smart Structures and Systems
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• 제15권3호
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• pp.735-749
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• 2015

The major difficulty of using Bayesian probabilistic inference for system identification is to obtain the posterior probability density of parameters conditioned by the measured response. The posterior density of structural parameters indicates how plausible each model is when considering the uncertainty of prediction errors. The Markov chain Monte Carlo (MCMC) method is a widespread medium for posterior inference but its convergence is often slow. The differential evolution adaptive Metropolis-Hasting (DREAM) algorithm boasts a population-based mechanism, which nms multiple different Markov chains simultaneously, and a global optimum exploration ability. This paper proposes an improved differential evolution adaptive Metropolis-Hasting algorithm (IDREAM) strategy to estimate the posterior density of structural parameters. The main benefit of IDREAM is its efficient MCMC simulation through its use of the adaptive Metropolis (AM) method with a mutation strategy for ensuring quick convergence and robust solutions. Its effectiveness was demonstrated in simulations on identifying the structural parameters with limited output data and noise polluted measurements.

• 한국수학교육학회지시리즈A:수학교육
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• 제55권2호
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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.