• Communications for Statistical Applications and Methods
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• 제10권1호
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• pp.203-210
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• 2003

In a standard Metropolis-type Monte Carlo simulation, the proposal distribution cannot be easily adapted to "local dynamics" of the target distribution. To overcome some of these difficulties, Duane et al. (1987) introduced the method of hybrid Monte Carlo(HMC) which combines the basic idea of molecular dynamics and the Metropolis acceptance-rejection rule to produce Monte Carlo samples from a given target distribution. In this paper, using the HMC within Gibbs sampler, an asymptotical estimate of the smoothing mean and a general solution to state space modeling in Bayesian framework is obtaineds obtained.

• Nuclear Engineering and Technology
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• 제54권3호
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• pp.790-802
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• 2022

Large-scale reactor simulation often requires the use of Monte Carlo calculation techniques to estimate important reactor parameters. One drawback of these Monte Carlo calculation techniques is they inevitably result in some uncertainty in calculated quantities. The present study includes parametric uncertainty quantification (UQ) and sensitivity analysis (SA) on the Advanced Test Reactor Critical (ATRC) facility housed at Idaho National Laboratory (INL) and addresses some complications due to Monte Carlo uncertainty when performing these analyses. This approach for UQ/SA includes consideration of Monte Carlo code uncertainty in computed sensitivities, consideration of uncertainty from directly measured parameters and a comparison of results obtained from brute-force Monte Carlo UQ versus UQ obtained from a surrogate model. These methodologies are applied to the uncertainty and sensitivity of k_eff for two sets of uncertain parameters involving fuel plate geometry and fuel plate composition. Results indicate that the less computationally-expensive method for uncertainty quantification involving a linear surrogate model provides accurate estimations for k_eff uncertainty and the Monte Carlo uncertainty in calculated k_eff values can have a large effect on computed linear model parameters for parameters with low influence on k_eff.

• 한국정보통신학회논문지
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• 제9권6호
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• pp.1341-1345
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• 2005

비선형이고 정규분포에 따르지 않는 state-space모형분석에서 순차적 몬테 칼로(SMC)는 유용한 도구 중의 하나이다. 모수와 시그럴을 동시에 추정하기 위해 Monte Carlo particle filters를 사용할 수가 있다. 그러나 SMC는 여러단계의 반복을 요구하는 특별한 particle filtering 기법을 필요로 하게 된다. 본 논문은 particle filtering과 순차적 hybrid Monte Carlo(SHMC)을 결합하는 방법을 제시하고자 한다. 실험을 위해 짱뚱어 자료를 사용하였다.

• Nuclear Engineering and Technology
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• 제49권6호
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• pp.1211-1218
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• 2017

Monte Carlo neutron transport codes generally use the method of successive generations to converge the fission source distribution to-and then maintain it at-the fundamental mode. Recently, a phenomenon called "clustering" has been noted, which produces fission distributions that are very far from the fundamental mode. In this study, a mathematical model of clustering in Monte Carlo has been developed. The model draws on previous work for continuous-time birth-death processes, as well as methods from the field of population genetics.

• 물과 미래
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• 제18권4호
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• pp.335-345
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• 1985

추계학적 이론을 근거로 하는 하천유량의 모의발생 모형에는 여러 가지가 있으며 이는 한정된 짧은 기간동안의 유량 실측치의 통계학적 특성을 재현시키는 일련의 장기적 유량자료를 인위적으로 발생시켜 수자원 시스템의 거동예측이나 조작기준을 보다 완벽하게 설정하기 위한 풍부한 인력 자료를 제공하자는 데 목적이 있다. 본 연구에서는 연유량의 모의발생에 주로 사용되는 Monte Carlo 모형을 연유량 자료를 구성하는 월별 하천유량의 발생에 적용 가능한가를 연구 검토하였다. 비교검토의 목적으로 실측된 월별 유량의 적정분포형을 설정한 후 Monte Carlo 방법에 의해 발생된 월별량과Autoregressive 모형중의 하나인 Thomas-Fiering의 다계절 모형에 의해 발생된 월류량의 통계학적 특성치의 실측치의 특성치와 비교하였다. 한편, 월유량 발생자료의 합성에 의한 연류량 자료의 특성치가 실측 월류량의 합성에 의한 월류량 특성치를 얼마나 잘 재현시키는가를 검사하기 위해 Monte Carlo 및 Thomas-Fiering 모형에 의해 발생시킨 연류량의 통계학적 특성치를 실측류량의 통계특성치와 비교평가하였다.

• 한국의학물리학회:학술대회논문집
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• 한국의학물리학회 2002년도 Proceedings
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• pp.113-115
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• 2002

The Monte Carlo simulation method is a numerical solution to a problem that models objects interacting with other objects or their environment based upon simple object-object or object-environment relationships. In spite of its great accuracy, It was turned away because of long calculation time to simulate a model. But, it is used to simulate a linear accelerator frequently with the advance of computer technology. To simulate linear accelerator in Monte Carlo simulations, there are many parameters needed to input to Monte Carlo code. These data can be supported by a linear accelerator manufacturer. Although the model of a linear accelerator is the same, a different characteristic property can be found. Thus, we performed a commissioning process of 6MV photon beam in Varian 2300C/D model with BEAM/EGS4 Monte Carlo code. The head geometry data were put into BEAM/EGS4 data. The mean energy and energy spread of the electron beam incident on the target were varied to match Monte Carlo simulations to measurements. TLDs (thermoluminescent dosimeter) and radiochromic films were employed to measure the absorbed dose in a water phantom. Beam profile was obtained in 40cm${\times}$40cm field size and Depth dose was in 10cm${\times}$10cm. At first, we compared the depth dose between measurements and Monte Carlo simulations varying the mean energy of an incident electron beam. Then, we compared the beam profile with adjusting the beam radius of the incident electron beam in Monte Carlo simulation. The results were found that the optimal mean energy was 6MV and beam radius of 0.1mm was well matched to measurements.

• Communications for Statistical Applications and Methods
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• 제5권1호
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• pp.55-67
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• 1998

Recent work by Shin and Sarkar (1996) examines model misspecification problems for nonstationary time series. Shin and Sarkar introduce a general regression model with integrated errors and one system of integrated regressors and discuss the limiting distributions of the OLS estimators and the usual OLS statistics such as $\hat{\sigma^2}$t, DW and $R^2$. We analyze three different model misspecification problems through a Monte Carlo study and investigate each model misspecification problem. Our Monte Carlo experiments show that DW and $R^2$ can be in general used as diagnostic tools to detect spurious regression, misspecification of nonstationary autoregressive and polynomial regression models.

• 대한화학회지
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• 제62권3호
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• pp.191-202
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• 2018

The aim of this work is to study Monte Carlo simulation of ethylene (co)polymerization over Ziegler-Natta catalyst as investigated by Chen et al. The results revealed that the Monte Carlo simulation was similar to sum square error (SSE) model to prediction of stage II and III of polymerization. In the case of activation stage (stage I) both model had slightly deviation from experimental results. The modeling results demonstrated that in homopolymerization, SSE was superior to predict polymerization rate in current stage while for copolymerization, Monte Carlo had preferable prediction. The Monte Carlo simulation approved the SSE results to determine role of each site in total polymerization rate and revealed that homopolymerization rate changed from site to site and order of center was different compared to copolymerization. The polymer yield was reduced by addition of hydrogen amount however there was no specific effect on uptake curve which was predicted by Monte Carlo simulation with good accuracy. In the case of copolymerization it was evolved that monomer chain length and monomer concentration influenced the rate of polymerization as rate of polymerization reduced from 1-hexene to 1-octene and increased when monomer concentration proliferate.

• 응용통계연구
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• 제13권2호
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• pp.541-562
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• 2000

일반화된 선형 혼합 모형(GLMM)은 자료가 계수의 형태로 나타나는 범주형 자료의 경우, 혹은 집락의 형태나 과산포된 비정규 자료, 또는 비선형 모형에 따르는 자료를 다루기 위한 모형 설정에 사용된다. 본 연구에서는 이에 대한 개요와 더불어, 이 모형의 적합을 위해 제시된 통계적 기법들중 의사가능도(quasi-likelihood: QL)를 이용한 추정 방법 및 Monte-Carlo 기법을 이용한 추정 방법들에 대해 조사하였다. 또한 GLMM에 대한 현재의 연구 방향 및 앞으로의 연구 가능 주제들에 대해서도 언급하였다.

• Journal of the Korean Statistical Society
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• 제36권3호
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• pp.435-445
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• 2007

This paper addresses for the first time the use of complete data information criteria in unsupervised learning of the Naive-Bayes model. A Monte Carlo study sets a large experimental design to assess these criteria, unusual in the Bayesian network literature. The simulation results show that complete data information criteria underperforms the Bayesian information criterion (BIC) for these Bayesian networks.