• Communications for Statistical Applications and Methods
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• v.3 no.3
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• pp.215-223
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• 1996

By using Tukey's generalized lambda distribution, appoximate posterior density is derived under the Bayes-empirical Bayes model. The sensitivity of posterior distribution to the hyperprior distribution is examined by using Tukey's generalized lambda distriburion which approximate many well-knmown distributions. Based upon Monte Varlo simulation studies it can be said that posterior distribution is sensitive to the cariance of the prior distribution and to the symmetry of the hyperprior distribution. Also posterior distribution is approximately obtained by using the following methods : Lindley method, Laplace method and Gibbs sampler method.

• Journal of Korea Multimedia Society
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• v.23 no.7
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• pp.883-890
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• 2020

In this paper, we propose a variational expectation-maximization algorithm that computes posterior probabilities from Latent Dirichlet Allocation (LDA) model. The algorithm approximates the intractable posterior distribution of a document term matrix generated from a corpus made up by 50 papers. It approximates the posterior by searching the local optima using lower bound of the true posterior distribution. Moreover, it maximizes the lower bound of the log-likelihood of the true posterior by minimizing the relative entropy of the prior and the posterior distribution known as KL-Divergence. The experimental results indicate that documents clustered to image classification and segmentation are correlated at 0.79 while those clustered to object detection and image segmentation are highly correlated at 0.96. The proposed variational inference algorithm performs efficiently and faster than Gibbs sampling at a computational time of 0.029s.

• Communications for Statistical Applications and Methods
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• v.11 no.1
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• pp.161-168
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• 2004

A single-index model is useful in fields which employ multidimensional regression models. Many methods have been developed in parametric and nonparametric approaches. In this paper, posterior inference is considered and a wavelet series is thought of as a function approximated to a true function in the single-index model. The posterior inference needs a prior distribution for each parameter estimated. A prior distribution of each coefficient of the wavelet series is proposed as a hierarchical distribution. A direction $\beta$ is assumed with a unit vector and affects estimate of the true function. Because of the constraint of the direction, a transformation, a spherical polar coordinate $\theta$, of the direction is required. Since the posterior distribution of the direction is unknown, we apply a Metropolis-Hastings algorithm to generate random samples of the direction. Through a Monte Carlo simulation we investigate estimates of the true function and the direction.

• PNF and Movement
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• v.12 no.1
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• pp.13-17
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• 2014

Purpose: To determine the normative data for the correlation of spinal, pelvic parameters with foot pressure in the young subjects. Methods: The subjects of this study were 39 patients in healthy adults. The Formetric-III was used to measure of spinal alignment. The pedoscan was used to measure of foot pressure. The correlation of trunk imbalance, trunk inclination, lateral deviation with foot pressure. The foot pressure measurement was consisted of maximal/mean pressure, weight contribution. Result: There was a negative correlation of trunk inclination with Max_R. There was a negative correlation of trunk inclination with Max_R. There was a positive correlation of trunk imbalance with Max_L. There was a positive correlation of lumbar lordosis with Mean_R_front, Lt. posterior weight distribution. There was a negative correlation of lumbar lordosis with Lt., Rt. in distribution There was a negative correlation of pelvic tilt with Mean_R_front, Lt. posterior weight distribution. There was a positive correlation of pelvic tilting with Rt. weight distribution, Lt. posterior weight distribution. There was a negative correlation of pelvic torsion with Lt. weight distribution, Rt. posterior weight distribution. There was a negative correlation of pelvic rotation with Lt. weight distribution, Lt. posterior weight distribution. Conclusion: The data obtained from the study may be used for future studies related to correlation of the spinal, pelvic deviation with foot pressure.

• Journal of the Korean Data and Information Science Society
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• v.22 no.6
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• pp.1183-1197
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• 2011

The prior distribution is the probability distribution we have before observing data. Using Bayes' rule, we can compute the posterior distribution, the new probability distribution, after observing data. Computing the posterior distribution is much easier than before by using Excel VBA macro. In addition, we can conveniently compute the successive updating posterior distributions after observing the independent and sequential outcomes. In this paper we compose some Excel VBA macros for applying Bayes' rule and give some examples.

• PNF and Movement
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• v.16 no.3
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• pp.345-353
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• 2018

Purpose: The purpose of the present study was to compare the effects of proprioceptive neuromuscular facilitation (PNF) and static stretching on weight distribution and flexibility for trunk flexion. Method: Sixty participants who had no musculoskeletal disorders were recruited from a local university within six months of this study. The participants were randomly assigned to a PNF stretching group (N=30) and a static stretching group (N=32). For the pre-and post-measurement design, the left-right weight distribution, anterior-posterior weight distribution, and finger-to-floor distance (FFD) were measured before and after the stretching interventions. Result: The FFD results were significantly improved after the interventions, regardless of the group differentiation (p<0.05). The PNF stretching intervention significantly increased the differences between anterior and posterior weight distribution compared to the static stretching group (p<0.05). Conclusions: Both the PNF and static stretching interventions could improve flexibility for trunk flexion mobility. Although the PNF intervention improved the weight distribution in the anterior-posterior direction, further research is required to investigate the various PNF interventions on left-and-right and anterior-posterior weight distribution.

• Proceedings of the Korean Society of Computational and Applied Mathematics Conference
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• 2003.09a
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• pp.13.2-13
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• 2003

We propose some properties of Bayesian fuzzy hypotheses testing by revision for prior possibility distribution and posterior possibility distribution using weighted fuzzy hypotheses versus on with loss function.

• Bulletin of the Korean Mathematical Society
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• v.44 no.2
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• pp.265-270
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• 2007

The modern approach for wavelets imposes a Bayesian prior model on the wavelet coefficients to capture the sparseness of the wavelet expansion. The idea is to build flexible probability models for the marginal posterior densities of the wavelet coefficients. In this note, we derive exact expressions for a popular model for the marginal posterior density.

• Journal of the Korea Safety Management & Science
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• v.13 no.1
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• pp.195-202
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• 2011

The study interprets each of three classification models based on Bath-Tub Failure Rate (BTFR), Extreme Value Distribution (EVD) and Conjugate Bayesian Distribution (CBD). The classification model based on BTFR is analyzed by three failure patterns of decreasing, constant, or increasing which utilize systematic management strategies for reliability of time. Distribution model based on BTFR is identified using individual factors for each of three corresponding cases. First, in case of using shape parameter, the distribution based on BTFR is analyzed with a factor of component or part number. In case of using scale parameter, the distribution model based on BTFR is analyzed with a factor of time precision. Meanwhile, in case of using location parameter, the distribution model based on BTFR is analyzed with a factor of guarantee time. The classification model based on EVD is assorted into long-tailed distribution, medium-tailed distribution, and short-tailed distribution by the length of right-tail in distribution, and depended on asymptotic reliability property which signifies skewness and kurtosis of distribution curve. Furthermore, the classification model based on CBD is relied upon conjugate distribution relations between prior function, likelihood function and posterior function for dimension reduction and easy tractability under the occasion of Bayesian posterior updating.

• Journal of Communications and Networks
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• v.7 no.4
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• pp.438-449
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• 2005

In this paper, we describes the information-theoretic approaches to sensor selection and sensor placement in sensor net­works for target localization and tracking. We have developed a sensor selection heuristic to activate the most informative candidate sensor for collaborative target localization and tracking. The fusion of the observation by the selected sensor with the prior target location distribution yields nearly the greatest reduction of the entropy of the expected posterior target location distribution. Our sensor selection heuristic is computationally less complex and thus more suitable to sensor networks with moderate computing power than the mutual information sensor selection criteria. We have also developed a method to compute the posterior target location distribution with the minimum entropy that could be achieved by the fusion of observations of the sensor network with a given deployment geometry. We have found that the covariance matrix of the posterior target location distribution with the minimum entropy is consistent with the Cramer-Rao lower bound (CRB) of the target location estimate. Using the minimum entropy of the posterior target location distribution, we have characterized the effect of the sensor placement geometry on the localization accuracy.