• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권10호
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• pp.5260-5275
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• 2019

In the process of constructing the traditional offensive and defensive game theory model, these are some shortages for considering the dynamic change of security risk problem. By analysing the critical indicators of the incomplete information game theory model, incomplete information attack and defense game theory model and the mathematical engineering method for solving Bayes-Nash equilibrium, the risk-averse income function for information assets is summarized as the problem of maximising the return of the equilibrium point. To obtain the functional relationship between the optimal strategy combination of the offense and defense and the information asset security probability and risk probability. At the same time, the offensive and defensive examples are used to visually analyse and demonstrate the incomplete information game and the Harsanyi conversion method. First, the incomplete information game and the Harsanyi conversion problem is discussed through the attack and defense examples and using the game tree. Then the strategy expression of incomplete information static game and the engineering mathematics method of Bayes-Nash equilibrium are given. After that, it focuses on the offensive and defensive game problem of unsafe information network based on risk aversion. The problem of attack and defense is obtained by the issue of maximizing utility, and then the Bayes-Nash equilibrium of offense and defense game is carried out around the security risk of assets. Finally, the application model in network security penetration and defense is analyzed by designing a simulation example of attack and defense penetration. The analysis results show that the constructed income function model is feasible and practical.

• Communications for Statistical Applications and Methods
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• 제20권4호
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• pp.321-327
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• 2013

Bayesian and empirical Bayesian methods have become quite popular in the theory and practice of statistics. However, the objective is to often produce an ensemble of parameter estimates as well as to produce the histogram of the estimates. For example, in insurance pricing, the accurate point estimates of risk for each group is necessary and also proper dispersion estimation should be considered. Well-known Bayes estimates (which is the posterior means under quadratic loss) are underdispersed as an estimate of the histogram of parameters. The adjustment of Bayes estimates to correct this problem is known as constrained Bayes estimators, which are matching the first two empirical moments. In this paper, we propose a way to apply the constrained Bayes estimators in insurance pricing, which is required to estimate accurately both location and dispersion. Also, the benefit of the constrained Bayes estimates will be discussed by analyzing real insurance accident data.

• Journal of the Korean Data and Information Science Society
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• 제25권3호
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• pp.685-696
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• 2014

In this paper, we develop Bayesian inference of the finite population mean with the assumption of posterior linearity rather than normality of the superpopulation in the presence of auxiliary information under the balanced loss function. We compare the performance of the optimal Bayes estimator under the balanced loss function with ones of the classical ratio estimator and the usual Bayes estimator in terms of the posterior expected losses, risks and Bayes risks.

• Journal of the Korean Statistical Society
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• 제29권4호
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• pp.435-441
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• 2000

Ko(1998, 1999) proposed certain flexible two-stage plans that could be served as one-step interim analysis in on-going clinical trials. The proposed Plans are optimal simultaneously in both a Bayes and a Neyman-Pearson sense. The Neyman-Pearson interpretation is that average expected sample size is being minimized, subject just to the two overall error rates $\alpha$ and $\beta$, respectively of first and second kind. The Bayes interpretation is that Bayes risk, involving both sampling cost and wrong decision losses, is being minimized. An example of this correspondence are given by using a binomial setting.

• Journal of the Korean Statistical Society
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• 제20권2호
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• pp.108-117
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• 1991

A parametric empirical Bayes procedure is proposed and studied to compare treatments simultaneously with the best. Minimum Bayes risk lower bounds are derived for an additive loss function, and their relationship with Bayesian simultaneous confidence lower bounds is given. For the proposed empirical Bayes procedure, the nominal confidence level both in Bayesian sense and in frequentist's sense is shown to be controlled asymptotically. For practical implementation, a measure of significance similar to f-value is suggested with an illustrative example.

• Communications for Statistical Applications and Methods
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• 제8권2호
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• pp.453-464
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• 2001

A commercial nuclear power station contains at least tow emergency diesel generators(EDG) to control the risk of severe core damage during station blackout accidents. Therefore, the reliability of the EDG's to start and load-run on demand must be maintained at a sufficiently high level. Probabilistic safety assessments(PSA) are increasingly being used to quantify the public risk of operating potentially hazardous systems such as nuclear power reactors. In this paper, to perform PSA, we will introduce three different types of data and use Bayes procedure to estimate the error rate of nuclear power plant EDG, and using practical examples, illustrate which method is more reasonable in our situation.

• Communications for Statistical Applications and Methods
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• 제6권1호
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• pp.107-116
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• 1999

We propose a sample design which minimize Bayes risk for cluster smpling in sampling inspection. We treat a pilot sample and an additional sample size as random variable. In addition we compute an appropriate cluster size for handling over-dispersion.

• Journal of the Korean Statistical Society
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• 제7권1호
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• pp.11-16
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• 1978

Bayesian procedures are in vogue to revise the parameter estimates of the forecasting model in the light of actual time series data. In this paper, we study the Bayes forecast for demand and the risk when (a) 'noise' and (b) mean demand rate in a constant process model have moderately non-normal probability distributions.

• 대한토목학회논문집
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• 제43권6호
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• pp.785-791
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• 2023

TBM 터널 프로젝트 내 불확실성으로 인한 사고를 예방하기 위해 리스크 관리는 필수적이다. 특히, 터널 막장면부터 지표면까지의 광범위한 피해를 초래할 수 있는 TBM 터널 붕괴는 더욱 신중히 관리되어야 한다. 또한, 각 TBM 터널 프로젝트의 시간과 비용의 제약으로 인해, 합리적 수준의 대응조치 계획을 수립하기 위한 리스크 우선순위를 도출할 필요가 있다. 이에 따라, 본 연구는 TBM 사고 사례조사를 통해 TBM 리스크 데이터베이스를 구축하였고, 베이즈 정리를 활용하여 지질요인의 TBM 터널 붕괴 리스크 우선순위를 도출하였다. 총 87건의 TBM 사고사례를 기반으로 3가지 사건과 5가지 지질요인을 포함한 TBM 리스크 데이터베이스가 구축되었다. 이때, 자갈층 지반, 고수압 함수대는 관련 사례 수가 적어 통계적 편향을 방지하기 위해 제외되었다. 데이터베이스에 베이즈 정리를 적용한 결과, 단층대와 연약지반은 TBM 터널 붕괴의 발생확률을 상당히 증가시키나, 그 외 3가지 지질요인(복합지반, 높은 상재압력, 팽창성 지반)은 붕괴와 낮은 상관성을 보였다. 결과적으로, 지질요인의 TBM 터널 붕괴 리스크 우선순위는 다음과 같다: 1) 단층대, 2) 연약지반, 3) 복합지반, 4) 높은 상재압력, 5) 팽창성 지반.

• Communications for Statistical Applications and Methods
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• 제15권2호
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• pp.283-292
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• 2008

커널밀도함수의 추정을 이용한 분류 문제에서 평활모수(smoothing parameter, bandwidth)의 선택은 핵심적으로 중요한 역할을 한다. 본 논문에서는 분류에서 베이즈 리스크를 최적화하기 위한 평활모수의 선택이 각 개별 확률밀도함수를 추정하기 위한 최적의 평활모수와 어떤 관계가 있는지 살펴보았다. 실제 상황에서 사용할 수 있는 평활모수의 선택 방법으로 붓스트랩(bootstrap)과 교차확인법(cross-validation)을 이용하는 것을 비교한 결과, 붓스트랩 방법은 Hall과 Kang (2005)에서 밝혀진 이론적인 성질에 부합하는 반면 교차확인법은 그렇지 못함을 확인하였다. 또한, 각 방법으로 정한 평활모수를 사용하여 오분류율을 조사해 본 결과에서도 붓스트랩 방법이 우월함을 알 수 있었다.