• Journal of Korean Society on Water Environment
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• v.36 no.5
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• pp.353-363
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• 2020

Hydrologic models can be classified into two types: those for understanding physical processes and those for predicting hydrologic quantities. This study deals with how to use the model to predict today's stream flow based on the system's knowledge of yesterday's state and the model parameters. In this regard, for the model to generate accurate predictions, the uncertainty of the parameters and appropriate estimates of the state variables are required. In this study, a relatively simple hydrologic partitioning model is proposed that can explicitly implement the hydrologic partitioning process, and the posterior distribution of the parameters of the proposed model is estimated using the Markov chain Monte Carlo approach. Further, the application method of the ensemble Kalman filter is proposed for updating the normalized soil moisture, which is the state variable of the model, by linking the information on the posterior distribution of the parameters and by assimilating the observed steam flow data. The stochastically and recursively estimated stream flows using the data assimilation technique revealed better representation of the observed data than the stream flows predicted using the deterministic model. Therefore, the ensemble Kalman filter in conjunction with the Markov chain Monte Carlo approach could be a reliable and effective method for forecasting daily stream flow, and it could also be a suitable method for routinely updating and monitoring the watershed-averaged soil moisture.

• Journal of Korea Port Economic Association
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• v.31 no.3
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• pp.1-14
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• 2015

This study utilizes Monte Carlo simulation to forecast the time charter rate of vessels, the three-month Libor interest rate, and the ship demolition price, to mitigate future uncertainties involving these factors. The simulation was performed 10,000 times to obtain an exact result. For the empirical analysis - based on considerations in ordering ships in 2010-a comparison between the Monte Carlo simulation-based stochastic discounted cash flow (DCF) method and traditional DCF methods was made. The analysis revealed that the net present value obtained through Monte Carlo simulation was lower than that obtained via regular DCF methods, alerting the owners to risks and preventing them from placing injudicious orders for ships. This research has implications in reducing the uncertainties that future shipping markets face, through the use of a stochastic DCF approach with relevant variables and probability methods.

• Nuclear Engineering and Technology
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• v.55 no.9
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• pp.3450-3463
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• 2023

In the two-step analysis of Pebble-Bed type High-Temperature Gas-Cooled Reactor (PB-HTGR), the lattice physics calculation for the generation of homogenized cross-sections is based on the fuel pebble. However, the randomly-dispersed fuel particles in the fuel pebble introduce double heterogeneity and randomness. Compared to the deterministic method, the Monte Carlo method which is flexible in geometry modeling provides a high-fidelity treatment. Therefore, the Monte Carlo code NECP-MCX is extended in this study to perform the lattice physics calculation of the PB-HTGR. Firstly, the capability for the simulation of randomly-dispersed media, using the explicit modeling approach, is developed in NECP-MCX. Secondly, the capability for the generation of the homogenized cross-section is also developed in NECP-MCX. Finally, simplified PB-HTGR problems are calculated by a two-step neutronics analysis tool based on Monte Carlo homogenization. For the pebble beds mixed by fuel pebble and graphite pebble, the bias is less than 100 pcm when compared to the high-fidelity model, and the bias is increased to 269 pcm for pebble bed mixed by depleted fuel pebble. Numerical results show that the Monte Carlo lattice physics calculation for the two-step analysis of PB-HTGR is feasible.

• 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.

• Journal of Information Technology Services
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• v.4 no.1
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• pp.129-139
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• 2005

This paper proposes a framework of Operational Risk-based Business Continuity System(ORBCS), and develops protection system for operational risk through operational risk assessment and loss distribution approach based on risk management guideline announced in the basel II. In order to find out financial operational risk, business processes of domestic bank are assorted by seven event factors and eight business activities so that we can construct the system. After we find out KRI(Key Risk Indicator) index, tasks and risks, we calculated risk possibility and expected cost by analyzing quantitative data, questionnaire and qualitative approach for AHP model from the past events. Furthermore, we can assume unexpected cost loss by using loss distribution approach presented in the basel II. Each bank can also assume expected loss distributions of operational risk by seven event factors and eight business activities. In this research, we choose loss distribution approach so that we can calculate operational risk. In order to explain number of case happened, we choose poisson distribution, log-normal distribution for loss cost, and estimate model for Monte-Carlo simulation. Through this process which is measured by operational risk. of ABC bank, we find out that loss distribution approach explains closer unexpected cost directly compared than internal measurement approach, and makes less unexpected cost loss.

• Proceedings of the Korean Statistical Society Conference
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• 2002.05a
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• pp.73-78
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• 2002

In this paper we develop a method for constructing a Bayesian HPD (highest probability density) interval of a ratio of two multivariate normal generalized variances. The method gives a way of comparing two multivariate populations in terms of their dispersion or spread, because the generalized variance is a scalar measure of the overall multivariate scatter. Fully parametric frequentist approaches for the interval is intractable and thus a Bayesian HPD(highest probability densith) interval is pursued using a variant of weighted Monte Carlo (WMC) sampling based approach introduced by Chen and Shao(1999). Necessary theory involved in the method and computation is provided.

• Structural Engineering and Mechanics
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• v.53 no.4
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• pp.819-831
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• 2015

In this paper, we propose a new method for taking into account uncertainties based on the projection on polynomial chaos. The new approach is used to determine the dynamic response of a spur gear system with uncertainty associated to gear system parameters and this uncertainty must be considered in the analysis of the dynamic behavior of this system. The simulation results are obtained by the polynomial chaos approach for dynamic analysis under uncertainty. The proposed method is an efficient probabilistic tool for uncertainty propagation. It was found to be an interesting alternative to the parametric studies. The polynomial chaos results are compared with Monte Carlo simulations.

• Journal of Korean Society for Quality Management
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• v.35 no.4
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• pp.159-165
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• 2007

Using statistical methods a change-point analysis of oil supply disruption is conducted. The statistical distribution of oil supply disruption is a weibull distribution. The detection of the change-point is applied to Bayesian method and weibull parameters are estimated through Markov chain monte carlo and parameter approach. The statistical approaches to the estimation for the change-point and weibull parameters is implemented with the sets of simulated and real data with small sizes of samples.

• Communications for Statistical Applications and Methods
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• v.24 no.5
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• pp.507-518
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• 2017

Volatility plays a crucial role in theory and applications of asset pricing, optimal portfolio allocation, and risk management. This paper proposes a combined model of autoregressive moving average (ARFIMA), generalized autoregressive conditional heteroscedasticity (GRACH), and skewed-t error distribution to accommodate important features of volatility data; long memory, heteroscedasticity, and asymmetric error distribution. A fully Bayesian approach is proposed to estimate the parameters of the model simultaneously, which yields parameter estimates satisfying necessary constraints in the model. The approach can be easily implemented using a free and user-friendly software JAGS to generate Markov chain Monte Carlo samples from the joint posterior distribution of the parameters. The method is illustrated by using a daily volatility index from Chicago Board Options Exchange (CBOE). JAGS codes for model specification is provided in the Appendix.

• Korean Management Science Review
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• v.21 no.3
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• pp.129-147
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• 2004

Scenario analysis for strategic planning, unlike most forecasting methods, provides a qualitative, contextual description of how the present will evolve into the future. It normally tries to identify a set of possible futures, each of whose occurrence is plausible but not assured. In this paper, we propose the use of Cross Impact Analysis(CIA) approach for scenario generation about the future of Korean IT environments. In this analysis, we classified IT environments into technical, social, legislative, and economic factor. And various variables and events were defined in each factor. From the survey collected from IT related experts, we acquire probability of occurrence and compatibility estimates of every possible pairs of events as input. Then 2 phase analysis is used in order to choice events with high probability of occurrence and generate scenario. Finally, after CIA using Monte Carlo simulation, a detail scenario for 2010 was developed. These scenario drawn from the CIA approach is a result considered by cross impacts of various events.