• 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.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.292-302
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• 2018

This study proposes a Bayesian stochastic frontier model that is well-suited to productivity/efficiency analysis particularly using panel data. A unique feature of our proposal is that both production frontier and efficiency are estimable for each individual firm and their linkage to various firm characteristics enriches our understanding of the source of productivity/efficiency. Empirical application of the proposed analysis to Human Capital Corporate Panel data enables identification and quantification of the effects of Human Resource factors on firm efficiency in tandem with those of firm types on production frontier. A comprehensive description of the Markov Chain Monte Carlo estimation procedure is forwarded to facilitate the use of our proposed stochastic frontier analysis.

• Communications for Statistical Applications and Methods
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• v.26 no.2
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• pp.131-148
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• 2019

Constant stress partially accelerated life tests are studied according to exponentiated Weibull distribution. Grounded on multiple censoring, the maximum likelihood estimators are determined in connection with unknown distribution parameters and accelerated factor. The confidence intervals of the unknown parameters and acceleration factor are constructed for large sample size. However, it is not possible to obtain the Bayes estimates in plain form, so we apply a Markov chain Monte Carlo method to deal with this issue, which permits us to create a credible interval of the associated parameters. Finally, based on constant stress partially accelerated life tests scheme with exponentiated Weibull distribution under multiple censoring, the illustrative example and the simulation results are used to investigate the maximum likelihood, and Bayesian estimates of the unknown parameters.

• 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.

• The Bulletin of The Korean Astronomical Society
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• v.45 no.1
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• pp.51.3-52
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• 2020

We present the status of the development of a Markov Chain Monte Carlo (MCMC) parameter estimation (PE) pipeline for compact binary coalescences (CBCs) with the Japanese KAGRA gravitational-wave (GW) detector. The pipeline is included in the KAGRA Algorithm Library (KAGALI). Basic functionalities are benchmarked from the LIGO Algorithm Library (LALSuite) but the KAGRA MCMC PE pipeline will provide a simpler, memory-efficient pipeline to estimate physical parameters from gravitational waves emitted from compact binaries consisting of black holes or neutron stars. Applying inspiral-merge-ringdown and inspiral waveforms, we performed simulations of various black hole binaries, we performed the code sanity check and performance test. In this talk, we present the situation of GW observation with the Covid-19 pandemic. In addition to preliminary PE results with the KAGALI MCMC PE pipeline, we discuss how we can optimize a CBC PE pipeline toward the next observation run.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.35 no.4
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• pp.393-400
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• 2011

In this study, a procedure for the inverse estimation of the fatigue life parameters of springs which utilize the field fatigue life test data is proposed to replace real test with the FEA on fatigue life prediction. The Bayesian approach is employed, in which the posterior distributions of the parameters are determined conditional on the accumulated life data that are routinely obtained from the regular tests. In order to obtain the accurate samples from the distributions, the Markov chain Monte Carlo (MCMC) technique is employed. The distributions of the parameters are used in the FEA for predicting the fatigue life in the form of a predictive interval. The results show that the actual fatigue life data are found well within the posterior predictive distributions.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.9
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• pp.1133-1140
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• 2013

A computer model is a useful tool that provides solution via physical modeling instead of expensive testing. In reality, however, it often does not agree with the experimental data owing to simplifying assumption and unknown or uncertain input parameters. In this study, a Bayesian approach is proposed to calibrate the computer model in a probabilistic manner using the measured data. The elasto-plastic analysis of a pyrotechnically actuated device (PAD) is employed to demonstrate this approach, which is a component that delivers high power in remote environments by the combustion of a self-contained energy source. A simple mathematical model that quickly evaluates the performance is developed. Unknown input parameters are calibrated conditional on the experimental data using the Markov Chain Monte Carlo algorithm, which is a modern computational statistics method. Finally, the results are applied to determine the reliability of the PAD.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.40 no.10
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• pp.895-900
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• 2016

Remaining useful life (RUL) estimation of the Li-ion battery has gained great interest because it is necessary for quality assurance, operation planning, and determination of the exchange period. This paper presents the RUL estimation of an Li-ion battery for an energy storage system using exponential function for the degradation model and Markov Chain Monte Carlo (MCMC) approach for parameter estimation. The MCMC approach is dependent upon information such as model initial parameters and input setting parameters which highly affect the estimation result. To overcome this difficulty, this paper offers a guideline for model initial parameters based on the regression result, and MCMC input parameters derived by comparisons with a thorough search of theoretical results.

• The Korean Journal of Applied Statistics
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• v.18 no.1
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• pp.1-13
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• 2005

Latent Class model has been considered recently by many researchers and practitioners as a tool for identifying heterogeneous segments or groups in a population, and grouping objects into the segments. In this paper we consider data on prostate cancer patients from Korean National Cancer Institute and propose a method for grouping prostate cancer patients by using latent class Poisson model. A Bayesian approach equipped with a Markov chain Monte Carlo method is used to overcome the limit of classical likelihood approaches. Advantages of the proposed Bayesian method are easy estimation of parameters with their standard errors, segmentation of objects into groups, and provision of uncertainty measures for the segmentation. In addition, we provide a method to determine an appropriate number of segments for the given data so that the method automatically chooses the number of segments and partitions objects into heterogeneous segments.

• The Korean Journal of Applied Statistics
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• v.30 no.6
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• pp.877-888
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• 2017

Spectral analysis is used to determine the frequency of time series data. We first determine the frequency of the series through the power spectrum or the periodogram and then calculate the period of a cycle that may exist in a time series. Estimating the frequency using a Bayesian technique has been developed and proven to be useful; however, the Bayesian estimator for the frequency cannot be analytically solved through mathematical equations and may be handled numerically or computationally. In this paper, we make an inference on the Bayesian frequency through both resampling a parameter by Markov chain Monte Carlo (MCMC) methods and resampling data by bootstrap methods for a time series. We take the Korean real estate price index as an example for Bayesian frequency estimation. We have found a difference in the periods between the sale price index and the long term rental price index, but the difference is not statistically significant.