• Journal of Digital Convergence
• /
• v.13 no.8
• /
• pp.295-300
• /
• 2015

In vocabulary recognition using an HMM model which models the prior distribution for the observation of a discrete probability distribution indicates the advantages of low computational complexity, but relatively low recognition rate. The Bayesian techniques to improve vocabulary recognition model, it is proposed using a convergence of two methods to improve recognition noise-canceling recognition. In this paper, using a convergence of the prior probability method and techniques of Bayesian posterior probability based on HMM remove noise and improves the recognition rate. The result of applying the proposed method, the recognition rate of 97.9% in vocabulary recognition, respectively.

• Journal of the Korean Statistical Society
• /
• v.28 no.4
• /
• pp.503-513
• /
• 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).

• The Korean Journal of Applied Statistics
• /
• v.16 no.1
• /
• pp.141-150
• /
• 2003

This paper presents the multiple testing method of an autoregressive parameter in stationary AR(1) model using the usual Bayes factor. As prior distributions of parameters in each model, uniform prior and noninformative improper priors are assumed. Posterior probabilities through the usual Bayes factors are used for the model selection. Finally, to check whether these theoretical results are correct, simulated data and real data are analyzed.

• Structural Engineering and Mechanics
• /
• v.57 no.1
• /
• pp.21-43
• /
• 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.

• Journal of Korean Society for Quality Management
• /
• v.23 no.2
• /
• pp.53-67
• /
• 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.

• Journal of the Korean Data and Information Science Society
• /
• v.18 no.1
• /
• pp.247-257
• /
• 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.

• Research in Mathematical Education
• /
• v.16 no.4
• /
• pp.233-244
• /
• 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.

• Proceedings of the Korea Society for Industrial Systems Conference
• /
• 2009.05a
• /
• pp.107-113
• /
• 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.

• The Mathematical Education
• /
• v.55 no.2
• /
• pp.193-208
• /
• 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.

• Water for future
• /
• v.17 no.4
• /
• pp.273-279
• /
• 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.