• Communications for Statistical Applications and Methods
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• v.22 no.2
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• pp.181-199
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• 2015

In a short history, RNA-seq data have established a revolutionary tool to directly decode various scenarios occurring on whole genome-wide expression profiles in regards with differential expression at gene, transcript, isoform, and exon specific quantification, genetic and genomic mutations, and etc. RNA-seq technique has been rapidly replacing arrays with seq-based platform experimental settings by revealing a couple of advantages such as identification of alternative splicing and allelic specific expression. The remarkable characteristics of high-throughput large-scale expression profile in RNA-seq are lied on expression levels of read counts, structure of correlated samples and genes, larger number of genes compared to sample size, different sampling rates, inevitable systematic RNA-seq biases, and etc. In this study, we will comprehensively review how robust Bayesian and non-parametric methods have a better performance than classical statistical approaches by explicitly incorporating such intrinsic RNA-seq specific features with flexible and more appropriate assumptions and distributions in practice.

• Nuclear Engineering and Technology
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• v.40 no.5
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• pp.375-386
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• 2008

Conventional static reliability analysis methods are inadequate for modeling dynamic interactions between components of a system. Various techniques such as dynamic fault tree, dynamic Bayesian networks, and dynamic reliability block diagrams have been proposed for modeling dynamic systems based on improvement of the conventional modeling methods. In this paper, we review these methods briefly and introduce dynamic nodes to the existing reliability graph with general gates (RGGG) as an intuitive modeling method to model dynamic systems. For a quantitative analysis, we use a discrete-time method to convert an RGGG to an equivalent Bayesian network and develop a software tool for generation of probability tables.

• Communications for Statistical Applications and Methods
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• v.29 no.5
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• pp.513-532
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• 2022

In this paper, the use of Bayes factors and the deviance information criterion for model selection are compared in a Bayesian accelerated life testing setup. In Bayesian accelerated life testing, the most used tool for model comparison is the deviance information criterion. An alternative and more formal approach is to use Bayes factors to compare models. However, Bayesian accelerated life testing models with more than one stressor often have mathematically intractable posterior distributions and Markov chain Monte Carlo methods are employed to obtain posterior samples to base inference on. The computation of the marginal likelihood is challenging when working with such complex models. In this paper, methods for approximating the marginal likelihood and the application thereof in the accelerated life testing paradigm are explored for dual-stress models. A simulation study is also included, where Bayes factors using the different approximation methods and the deviance information are compared.

• Journal of Environmental Science International
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• v.20 no.12
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• pp.1541-1551
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• 2011

This study applied the Bayesian method for the quantification of the parameter uncertainty of spatial linear mixed model in the estimation of the spatial distribution of probability rainfall. In the application of Bayesian method, the prior sensitivity analysis was implemented by using the priors normally selected in the existing studies which applied the Bayesian method for the puppose of assessing the influence which the selection of the priors of model parameters had on posteriors. As a result, the posteriors of parameters were differently estimated which priors were selected, and then in the case of the prior combination, F-S-E, the sizes of uncertainty intervals were minimum and the modes, means and medians of the posteriors were similar to the estimates using the existing classical methods. From the comparitive analysis between Bayesian and plug-in spatial predictions, we could find that the uncertainty of plug-in prediction could be slightly underestimated than that of Bayesian prediction.

• The Journal of Engineering Research
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• v.2 no.1
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• pp.49-56
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• 1997

In emission computed tomography, statistical reconstruction methods in the context of a Bayesian framework have been a topic of interest over the last decade. This was mainly due to the fact that Bayesian approaches can incorporate a priori information into the reconstruction algorithm. Although these approaches can exhibit good performance, their applications to the clinic is hindered mainly by their high computational cost. On the other hand, the speed and simplicity of the filtered backprojection (FBP) algorithm have led to its widespread use in most clinical applications. In this work, we use spline models, which have been quite useful in Bayesian reconstruction, as regularizers for high-frequency apodization in FBP algorithm and show that the effects of using spline models as priors in Bayesian reconstruction can also be achieved in FBP reconstruction.

• Journal of Biomedical Engineering Research
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• v.22 no.4
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• pp.311-319
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• 2001

Statistical reconstruction methods in the context of a Bayesian framework have played an important role in emission tomography since they allow to incorporate a priori information into the reconstruction algorithm. Given the ill-posed nature of tomographic inversion and the poor quality of projection data, the Bayesian approach uses regularizers to stabilize solutions by incorporating suitable prior models. In this work we show that, while the quantitative performance of the standard filtered backprojection (FBP) algorithm is not as good as that of Bayesian methods, the application of spline-regularized smoothing to the sinogram space can make the FBP algorithm improve its performance by inheriting the advantages of using the spline priors in Bayesian methods. We first show how to implement the spline-regularized smoothing filter by deriving mathematical relationship between the regularization and the lowpass filtering. We then compare quantitative performance of our new FBP algorithms using the quantitation of bias/variance and the total squared error (TSE) measured over noise trials. Our numerical results show that the second-order spline filter applied to FBP yields the best results in terms of TSE among the three different spline orders considered in our experiments.

• Journal of KIISE:Software and Applications
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• v.29 no.11
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• pp.775-784
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• 2002

Microarray data, obtained from DNA chip technologies, is the measurement of the expression level of thousands of genes in cells or tissues. It is used for gene function prediction or cancer diagnosis based on gene expression patterns. Among diverse methods for data analysis, the Bayesian network represents the relationships among data attributes in the form of a graph structure. This property enables us to discover various relations among genes and the characteristics of the tissue (e.g., the cancer type) through microarray data analysis. However, most of the present microarray data sets are so sparse that it is difficult to apply general analysis methods, including Bayesian networks, directly. In this paper, we harness an efficient structural learning algorithm and data dimensionality reduction in order to analyze microarray data using Bayesian networks. The proposed method was applied to the analysis of real microarray data, i.e., the NC160 data set. And its usefulness was evaluated based on the accuracy of the teamed Bayesian networks on representing the known biological facts.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.1049-1068
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• 2014

Autoregressive models are used to analyze an univariate time series data; however, these methods can be inappropriate when a structural break appears in a time series since they assume that a trend is consistent. Threshold autoregressive models (popular regime-switching models) have been proposed to address this problem. Recently, the models have been extended to two regime-switching models with delay parameter. We discuss two regime-switching threshold autoregressive models from a Bayesian point of view. For a Bayesian analysis, we consider a parametric threshold autoregressive model and a nonparametric threshold autoregressive model using Dirichlet process prior. The posterior distributions are derived and the posterior inferences is performed via Markov chain Monte Carlo method and based on two Bayesian threshold autoregressive models. We present a simulation study to compare the performance of the models. We also apply models to gross domestic product data of U.S.A and South Korea.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.167-170
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• 2003

We develop semiparametric methods for matched case-control studies using regression splines. Three methods are developed: an approximate crossvalidation scheme to estimate the smoothing parameter inherent in regression splines, as well as Monte Carlo Expectation Maximization (MCEM) and Bayesian methods to fit the regression spline model. We compare the approximate cross-validation approach, MCEM and Bayesian approaches using simulation, showing that they appear approximately equally efficient, with the approximate cross-validation method being computationally the most convenient. An example from equine epidemiology that motivated the work is used to demonstrate our approaches.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.318-328
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• 2020

Letter of Credit (L/C) is currently a very popular international settlement method frequently used in international trade processes amongst countries around the globe. Compared with other international settlement methods, however, L/C has some obvious shortcomings. Firstly, it is not easy to use due to the sophisticated processes its usage involves. Secondly, it is sometimes accompanied by a few risks and some uncertainty. Thus, highly efficient methods need to be used to assess and control these risks. To begin with, FAHP and KMV methods are used to resolve the problem of incomplete information associated with L/C and then, on this basis, Bayesian game theory is used in order to make more scientific and reasonable decisions with respect to international trade.