• Nuclear Engineering and Technology
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• v.49 no.6
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• pp.1172-1180
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• 2017

The application of Monte Carlo (MC) to large-scale fixed-source problems has recently become possible with new hybrid methods that automate generation of parameters for variance reduction techniques. Two common variance reduction techniques, weight windows and source biasing, have been automated and popularized by the consistent adjoint-driven importance sampling (CADIS) method. This method uses the adjoint solution from an inexpensive deterministic calculation to define a consistent set of weight windows and source particles for a subsequent MC calculation. One of the motivations for source consistency is to avoid the splitting or rouletting of particles at birth, which requires computational resources. However, it is not always possible or desirable to implement such consistency, which results in inconsistent source biasing. This paper develops an original framework that mathematically expresses the coupling of the weight window and source biasing techniques, allowing the authors to explore the impact of inconsistent source sampling on the variance of MC results. A numerical experiment supports this new framework and suggests that certain classes of problems may be relatively insensitive to inconsistent source sampling schemes with moderate levels of splitting and rouletting.

• Communications for Statistical Applications and Methods
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• v.8 no.1
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• pp.257-269
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• 2001

The simulation is used to estimate an overflow probability in a stable parallel network with coupled inputs. Since the general simulation needs extremely many trials to obtain such a small probability, the fast simulation is proposed to reduce trials instead. By using the Cramer’s theorem, we first obtain an optimally changed measure under which the variance of the estimator is minimized. Then, we use it to derive an importance sampling estimator of the overflow probability which enables us to perform the fast simulation.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.39-47
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• 2006

The Bayesian approach to multiple testing procedure for one sample testing problem proposed by Scott and Berger (2003) is extended to two-sample comparison problem in microarray experiments. The prior distribution of each gene's mean for one sample is given conditionally on the corresponding gene's mean for the other sample. Posterior distributions of interesting parameters are derived and estimated based on an importance sampling method. A simulated example is given for illustration.

• Journal of the Korean Data and Information Science Society
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• v.15 no.1
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• pp.227-237
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• 2004

In this paper we presented a Bayesian inference approach for estimating the location and scale parameters of the lognormal distribution using iterative Gibbs sampling algorithm. We also presented estimation of location parameter by two non iterative methods, importance sampling and weighted bootstrap assuming scale parameter as known. The estimates by non iterative techniques do not depend on the specification of hyper parameters which is optimal from the Bayesian point of view. The estimates obtained by more sophisticated Gibbs sampler vary slightly with the choices of hyper parameters. The objective of this paper is to illustrate these tools in a simpler setup which may be essential in more complicated situations.

• Proceedings of the Korea Society for Simulation Conference
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• 1997.04a
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• pp.8-8
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• 1997

최근 시뮬레이션은 높은 신뢰도를 요구하는 통신망시스템이나, 높음 품질수준을 요 구하는 제조시스템의 분석 및 설계에 적용되어지고 있다. 이러한 신뢰도가 높은 시스템에 대한 시뮬레이션 적용의 난제는 실제 시스템과 시뮬레이션 모형이 얼마나 정확히 모델링을 하는가 하는 문제와 실제 모델링을 하여 시뮬레이션을 수행하여 얼마나 빠른 시간내에 정확 히 결과를 산출해 낼 수 있는가를 하는 것이다. 이러한 문제점을 극복하기 위해서 속산시뮬 레이션(fast simulation) 기법들이 연구되고 있다. 그러한 기법들로 Importance Sampling (IS), Regenerative Method (RM), Parallel Simulation 등이 연구되고 있다. IS는 잘 알려진 분산축소 기법으로 속산시뮬레이션을 위하여 많이 사용되고 있으나 실제로 복잡한 모델에 적용하기에는 많은 어려움이 따른다. 그 이유는 최적 표본분포 (Optimal Sampling Distribution)를 찾기 위한 방법이 정형화되어 있지 않아 모델마다 최적표본분포를 유사하게 추정해야 하는 어려움이 따르기 때문이다. 이러한 단점을 극복하기 위하여 Nonparametric Improtance Sampling을 제안하고 실제로 M/M/1 대기행렬 모형에 적용하여 보았다.

• Journal of the Chungcheong Mathematical Society
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• v.21 no.4
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• pp.573-583
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• 2008

In this paper, we introduce the techniques of estimating the probability of failure in reliability analysis. The basic idea of each technique is explained and drawbacks of these techniques are examined.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.25 no.5A
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• pp.668-673
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• 2000

In this paper, not only the problems which could not be solved with Conventional Importance Sampling and Improved Importance Sampling of the early simulation method, and but also the improvements obtained in terms of computer run-time were studied, by applying the central moment algorithm to the digital communication channels. That is, the channel performance evaluation is done for obtaining the cumulative probability function of the statistical characteristics of received signal with estimating the central moment of the received signal mixed the noise in the digital communication receiver. We confirm the simulation run-time after we implemented the quaternary phase shift keying(QPSK) satellite communication channels using the Signal Processing Worksystem(SPW) of the Cadence incorporation to verify the suggested algorithm.

• Journal of the Korean Statistical Society
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• v.32 no.4
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• pp.359-371
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• 2003

We consider the probability that the total population of a stable Jackson network reaches a given large value. By using the fluid limit of the reversed network, we derive new upper and lower bounds on this probability, which are sharper than those in Glasserman and Kou (1995). In particular, the improved lower bound is useful for analyzing the performance of an importance sampling estimator for the overflow probability in Jackson tandem networks. Bounds on the expected time to overflow are also obtained.

• Journal of the Korean Statistical Society
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• v.26 no.4
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• pp.493-505
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• 1997

The analysis of binary data appears to many areas such as statistics, biometrics and econometrics. In many cases, data are often collected in which some observations are incomplete. Assume that the missing covariates are missing at random and the responses are completely observed. A method to Bayesian analysis of the binary regression model with incomplete data is presented. In particular, the desired marginal posterior moments of regression parameter are obtained using Meterpolis algorithm (Metropolis et al. 1953) within Gibbs sampler (Gelfand and Smith, 1990). Also, we compare logit model with probit model using Bayes factor which is approximated by importance sampling method. One example is presented.

• Journal of Genetic Medicine
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• v.11 no.2
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• pp.43-48
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• 2014

Chorionic villus sampling has gained importance as a tool for early cytogenetic diagnosis with a shift toward first trimester screening. First trimester screening using nuchal translucency and biomarkers is effective for screening. Chorionic villus sampling generally is performed at 10-12 weeks by either the transcervical or transabdominal approach. There are two methods of analysis; the direct method and the culture method. While the direct method may prevent maternal cell contamination, the culture method may be more representative of the true fetal karyotype. There is a concern for mosaicism which occurs in approximately 1% of cases, and mosaic results require genetic counseling and follow-up amniocentesis or fetal blood sampling. In terms of complications, procedure-related pregnancy loss rates may be the same as those for amniocentesis when undertaken in experienced centers. When the procedure is performed after 9 weeks gestation, the risk of limb reduction is not greater than the risk in the general population. At present, chorionic villus sampling is the gold standard method for early fetal karyotyping; however, we anticipate that improvements in noninvasive prenatal testing methods, such as cell free fetal DNA testing, will reduce the need for invasive procedures in the near future.