• 제목/요약/키워드: Prior Probability

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HMM을 기반으로 한 사전 확률의 문제점을 해결하기 위해 베이시안 기법 어휘 인식 모델에의 사후 확률을 융합한 잡음 제거 (Noise Removal using a Convergence of the posteriori probability of the Bayesian techniques vocabulary recognition model to solve the problems of the prior probability based on HMM)

  • 오상엽
    • 디지털융복합연구
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    • 제13권8호
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    • pp.295-300
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    • 2015
  • 사전 확률분포를 모델링하는 HMM을 사용하는 어휘 인식에서 인식 어휘의 모델들의 대한 인식 확률이 이산적인 분포를 나타내며 인식을 위한 계산량이 적은 장점이 있지만 인식률을 계산했을 때 상대적으로 낮은 단점이 있다. 이를 개선하기 위하여 베이시안 기법 어휘 인식 모델을 융합한 잡음 제거 인식률 향상을 제안한다. 본 논문은 베이시안 기법 어휘 인식을 위한 모델 구성을 베이시안 기법의 최적화한 인식 모델을 구성하였다. HMM을 기반으로 한 사전 확률 방법과 베이시안 기법인 사후확률을 융합하여 잡음을 제거하고 인식률을 향상시켰다. 본 논문에서 제안한 방법을 적용한 결과 어휘 인식률에서 98.1%의 인식률을 나타내었다.

ARMA Model Identification Using the Bayes Factor

  • Son, Young-Sook
    • Journal of the Korean Statistical Society
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    • 제28권4호
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    • pp.503-513
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    • 1999
  • The Bayes factor for the identification of stationary ARM(p,q) models is exactly computed using the Monte Carlo method. As priors are used the uniform prior for (\ulcorner,\ulcorner) in its stationarity-invertibility region, the Jefferys prior and the reference prior that are noninformative improper for ($\mu$,$\sigma$\ulcorner).

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AR(1)모형에서 자기회귀계수의 다중검정을 위한 베이지안방법 (Bayesian Method for the Multiple Test of an Autoregressive Parameter in Stationary AR(L) Model)

  • 김경숙;손영숙
    • 응용통계연구
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    • 제16권1호
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    • pp.141-150
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    • 2003
  • 본 논문은 베이즈인자(Bayes factor)를 이용하여 정상(stationary) AR(1)모형의 자기회귀계수에 대해 다중검정하는 방법을 제시한다. 모수들에 대한 사전분포로는 무정보 사전분포(noninformative prior distribution)를 가정한다. 이러한 경우에 통상적으로 사용되는 베이즈인자를 근사없이 정확히 계산하여 각 모형에 대한 사후확률(posterior probability)을 얻는다. 최종적으로 모의실험 자료 및 실제 자료에 적용하여 이론의 결과가 잘 부합되는지를 검토한다.

Identification of flexible vehicle parameters on bridge using particle filter method

  • Talukdar, S.;Lalthlamuana, R.
    • Structural Engineering and Mechanics
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    • 제57권1호
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    • pp.21-43
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    • 2016
  • A conditional probability based approach known as Particle Filter Method (PFM) is a powerful tool for system parameter identification. In this paper, PFM has been applied to identify the vehicle parameters based on response statistics of the bridge. The flexibility of vehicle model has been considered in the formulation of bridge-vehicle interaction dynamics. The random unevenness of bridge has been idealized as non homogeneous random process in space. The simulated response has been contaminated with artificial noise to reflect the field condition. The performance of the identification system has been examined for various measurement location, vehicle velocity, bridge surface roughness factor, noise level and assumption of prior probability density. Identified vehicle parameters are found reasonably accurate and reconstructed interactive force time history with identified parameters closely matches with the simulated results. The study also reveals that crude assumption of prior probability density function does not end up with an incorrect estimate of parameters except requiring longer time for the iterative process to converge.

성장곡선모형의 판별분석에서 균형이차분류법의 적용 (An Application of the Balanced Quadratic Classification Rule on the Discriminant Analysis in Growth Curve Model)

  • 심규박
    • 품질경영학회지
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    • 제23권2호
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    • pp.53-67
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    • 1995
  • The problem considered here is to find the optimal discriminant analysis method in growth curve model. It has been studied how to find correct prior probability for the effective classification in discriminant analysis. We use the balanced condition to calculate prior probability. From the informative simulation study, new classification rule for the growth curve model is suggested. The suggested classification rule has better classification result than the other previously suggested method in terms of error rate criterion.

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Noninformative Priors for the Common Shape Parameter in the Gamma Distributions

  • Kang, Sang-Gil;Kim, Dal-Ho;Lee, Woo-Dong
    • Journal of the Korean Data and Information Science Society
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    • 제18권1호
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    • pp.247-257
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    • 2007
  • In this paper, we develop the noninformative priors for the common shape parameter in the gamma distributions. We develop the matching priors and reveal that the second order matching prior does not exist. It turns out that the one-at-a-time reference prior and the two group reference prior satisfy a first order probability matching criterion. Some simulation study is peformed.

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Creativity Development in Probability through Debate

  • Oh, Taek-Keun;Lee, Kyeong Hwa
    • 한국수학교육학회지시리즈D:수학교육연구
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    • 제16권4호
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    • pp.233-244
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    • 2012
  • The purpose of this study is to investigate the relationship between creativity development and debate in solving a probability task. We developed the probability task with instructional strategies facilitating debating among students. 33 students in grade 11 who were identified as gifted participated in this study. The findings indicated that debating leads students to critical and reflective thinking on prior learning regarding probability concepts, which nurtured creative ideas on sample space.

Noninformative Priors for Step Stress Accelerated Life Tests in Exponential Distribution

  • 이우동;박홍경
    • 한국산업정보학회:학술대회논문집
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    • 한국산업정보학회 2009년도 춘계학술대회 미래 IT융합기술 및 전략
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    • pp.107-113
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    • 2009
  • This paper deals with noninformative priors for such as Jeffres' prior, reference prior and probability matching prior for scale parameter of exponential distribution when the data are collected in multiple step stress accelerated life tests. We find the noninformative priors for this model and show that the reference prior satisfies first order matching criterion. Using artificial data, we perform Bayesian analysis for proposed priors.

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우도원리에 대한 분석과 그에 따른 교육적 시사점에 대한 연구 (A Study on Analysis of Likelihood Principle and its Educational Implications)

  • 박선용;윤형석
    • 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.

한반도에 대한 태풍내습확률 산정에 관한 연구 (A Study on the Probabilistic Prediction of Typhoons Approaching the Korean-Peninsula)

  • 박준일;유희정;이배호
    • 물과 미래
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    • 제17권4호
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    • pp.273-279
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    • 1984
  • An attempt is made to present a method of prediction for typhoons apporaching the Korean-peninsula. The method is based upon the Bayesian theorem to improve the observed (prior) probabilities of typhoons approaching the Korean sea area incorporating conditional probability. A total of 248 typhoons is collected and analyzed to establish prior probability and conditional probability according to the defined procedure. The typhoons used are those which encompassed the western Pacific area to which the Korean-peninsula is subjected. The results of examplary computations suggest that the presented method is promising for predicting approaching typhoons.

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