• Computers and Concrete
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• v.13 no.5
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• pp.659-677
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• 2014

A Bayesian response surface updating procedure is applied in order to update the parameters of the covariance function of a random field for concrete properties based on a limited number of available measurements. Formulas as well as a numerical algorithm are presented in order to update the parameters of response surfaces using Markov Chain Monte Carlo simulations. The parameters of the covariance function are often based on some kind of expert judgment due the lack of sufficient measurement data. However, a Bayesian updating technique enables to estimate the parameters of the covariance function more rigorously and with less ambiguity. Prior information can be incorporated in the form of vague or informative priors. The proposed estimation procedure is evaluated through numerical simulations and compared to the commonly used least square method.

• Steel and Composite Structures
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• v.51 no.6
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• pp.697-712
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• 2024

In this paper, the dynamic flexural stiffness of concrete-filled steel tubular (CFST) members is investigated based on vibration modal testing and a Bayesian model updating procedure. To reflect the actual service states of CFST members, a 3-stage modal testing procedure is developed for 6 circular CFST beam-columns, in which the modal parameters of the specimens under varying axial load levels are extracted. In the model updating procedure, a Timoshenko beam element model is first established, in which the influence of shear deformation and rotational inertia are incorporated. Subsequently, a 2-round Bayesian model updating strategy is proposed to calculate the dynamic flexural stiffness of the specimens, which could effectively consider the influence of physical constraints in the updating process and achieve reasonably well results. Analysis of the updating results shows that with the increase of the axial load level, degradation of the flexural stiffness is significantly influenced by the load eccentricity. It shows that the cracking of the core concrete is the primary reason for the flexural stiffness degradation of CFST beam-columns. Finally, based on comparison with equations proposed by several design standards, the calculation methods for the dynamic flexural stiffness of CFST members is recommended.

• Communications for Statistical Applications and Methods
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• v.8 no.2
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• pp.417-426
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• 2001

This paper addresses the nonparametric Bayesian estimation for the exponential populations under type II censoring. The Dirichlet process prior is used to provide nonparametric Bayesian estimates of parameters of exponential populations. In the past, there have been computational difficulties with nonparametric Bayesian problems. This paper solves these difficulties by a Gibbs sampler algorithm. This procedure is applied to a real example and is compared with a classical estimator.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1149-1160
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• 2011

Model selection and hypothesis testing problems in Bayesian inference are still debated between scholars. Bayesian factors traditionally used as a criterion in Bayesian hypothesis testing and model selection, are easy to understand but sometimes hard to compute. In addition, there are other model selection criterions such as DIC(Deviance Information Criterion) by Spiegelhalter et al. (2002) and Bayesian P-values for testing. In this paper, we briefly introduce the Bayesian hypothesis testing and model selection procedure. In addition we have applied a Bayesian inference to Swiss banknote data by a fitting logistic regression model and computing several test statistics to see if they provide consistent results.

• Journal of Korea Multimedia Society
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• v.11 no.12
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• pp.1625-1634
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• 2008

In this study, a modified Fuzzy C-Means algorithm with Gaussian weights (MFCM_GW) is presented for the problem of nonlinear blind channel equalization. The proposed algorithm searches for the optimal channel output states of a nonlinear channel based on received symbols. In contrast to conventional Euclidean distance in Fuzzy C-Means (FCM), the use of the Bayesian likelihood fitness function and the Gaussian weighted partition matrix is exploited in this method. In the search procedure, all possible sets of desired channel states are constructed by considering the combinations of estimated channel output states. The set of desired states characterized by the maxima] value of the Bayesian fitness is selected and updated by using the Gaussian weights. After this procedure, the Bayesian equalizer with the final desired states is implemented to reconstruct transmitted symbols. The performance of the proposed method is compared with those of a simplex genetic algorithm (GA), a hybrid genetic algorithm (GA merged with simulated annealing (SA):GASA), and a previously developed version of MFCM. In particular, a relative]y high accuracy and a fast search speed have been observed.

• Journal of the Korean Statistical Society
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• v.10
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• pp.128-139
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• 1981

The Bayesian sequential estimation problem for k parameters exponential families is considered using loss related to the Fisher information. Tractable expressions for the Bayes estimator and the posterior expected loss are found, and the myopic or one-step-ahead stopping rule is defined. Sufficient conditions are given for optimality of the myopic procedure, and the myopic procedure is shown to be asymptotically optimal in all cases considered.

• Journal of Korean Society for Quality Management
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• v.28 no.4
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• pp.67-74
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• 2000

A Bayesian burn-in procedure is developed for imited failure populations in which defective items fail soon after they are put in operation and non-defective ones never fail during he technical life of the items. Sequential schemes guaranteeing pre-specified outgoing quality of a product are derived based on prior information on the quality of a product and accumulated failure information up to the decision point. A numerical example is also provided.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.229-236
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• 2004

This article presents a multiple comparison ranking procedure for several products of the Poisson rates. A preference probability matrix that warrants the optimal comparison ranking is introduced. Using a Bayesian Monte Carlo method, we develop simulation-based procedure to estimate the matrix and obtain the optimal ranking via a row-sum scores method. Necessary theory and two illustrative examples are provided.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6A
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• pp.561-572
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• 2010

This paper is a study about application of recently proposed Performance Based Mixture Design (PBMD) for design of high strength concrete (HSC) to obtain HSC mix proportion that satisfies required performances. The PBMD method which uses Satisfaction curve based on a Bayesian method is a performance oriented concrete mix proportion design procedure easily applicable to any condition and environment for a possible replacement to the current prescriptive design standards. Based on extensive experimental results obtained for various materials and performance parameters of HSC, the application feasibility of the developed PBMD procedure for HSC has been verified. Also, the proposed PBMD procedure has been used to perform application examples to obtain desired target performances of HSC with optimum concrete mixture proportions using locally available materials, local environmental conditions, and available concrete production technologies. The validity and precision of HSC mix proportion design obtained using the PBMD method is verified with the experimental and ACI presented results to check the feasibility for actual design usage.

• Communications for Statistical Applications and Methods
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• v.12 no.1
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• pp.139-147
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• 2005

It is interesting to locate homogeneous segments within a DNA sequence. Suppose that the DNA sequence has segments within which the observations follow the same residue frequency distribution, and between which observations have different distributions. In this setting, change points correspond to the end points of these segments. This article explores the use of a binary segmentation procedure in detecting the change points in the DNA sequence. The change points are determined using a sequence of nested hypothesis tests of whether a change point exists. At each test, we compare no change-point model with a single change-point model by using the Bayesian information criterion. Thus, the method circumvents the computational complexity one would normally face in problems with an unknown number of change points. We illustrate the procedure by analyzing the genome of the bacteriophage lambda.