• 제목/요약/키워드: Bayesian robustness

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Minimizing Weighted Mean of Inefficiency for Robust Designs

  • Seo, Han-Son
    • Communications for Statistical Applications and Methods
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    • 제15권1호
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    • pp.95-104
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    • 2008
  • This paper addresses issues of robustness in Bayesian optimal design. We may have difficulty applying Bayesian optimal design principles because of the uncertainty of prior distribution. When there are several plausible prior distributions and the efficiency of a design depends on the unknown prior distribution, robustness with respect to misspecification of prior distribution is required. We suggest a new optimal design criterion which has relatively high efficiencies across the class of plausible prior distributions. The criterion is applied to the problem of estimating the turning point of a quadratic regression, and both analytic and numerical results are shown to demonstrate its robustness.

Robustness in the Hierarchical Bayes Estimation of Normal Means

  • Kim, Dal-Ho;Park, Jin -Kap
    • Communications for Statistical Applications and Methods
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    • 제6권2호
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    • pp.511-522
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    • 1999
  • The paper considers the problem of robustness in hierarchical bayesian models. In specific we address Bayesian robustness in the estimation of normal means. We provide the ranges of the posterior means under $\varepsilon$-contamination class as well as the density ratio class of priors. For the class of priors that are uniform over a specified interval we investigate the sensitivity as to the choice of the intervals. The methods are illustrated using the famous baseball data of Efron and Morris(1975).

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A Robust Bayesian Probabilistic Matrix Factorization Model for Collaborative Filtering Recommender Systems Based on User Anomaly Rating Behavior Detection

  • Yu, Hongtao;Sun, Lijun;Zhang, Fuzhi
    • KSII Transactions on Internet and Information Systems (TIIS)
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    • 제13권9호
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    • pp.4684-4705
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    • 2019
  • Collaborative filtering recommender systems are vulnerable to shilling attacks in which malicious users may inject biased profiles to promote or demote a particular item being recommended. To tackle this problem, many robust collaborative recommendation methods have been presented. Unfortunately, the robustness of most methods is improved at the expense of prediction accuracy. In this paper, we construct a robust Bayesian probabilistic matrix factorization model for collaborative filtering recommender systems by incorporating the detection of user anomaly rating behaviors. We first detect the anomaly rating behaviors of users by the modified K-means algorithm and target item identification method to generate an indicator matrix of attack users. Then we incorporate the indicator matrix of attack users to construct a robust Bayesian probabilistic matrix factorization model and based on which a robust collaborative recommendation algorithm is devised. The experimental results on the MovieLens and Netflix datasets show that our model can significantly improve the robustness and recommendation accuracy compared with three baseline methods.

Local Sensitivity Analysis using Divergence Measures under Weighted Distribution

  • Chung, Younshik;Dey, Dipak K.
    • Journal of the Korean Statistical Society
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    • 제30권3호
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    • pp.467-480
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    • 2001
  • This paper considers the use of local $\phi$-divergence measures between posterior distributions under classes of perturbations in order to investigate the inherent robustness of certain classes. The smaller value of the limiting local $\phi$-divergence implies more robustness for the prior or the likelihood. We consider the cases when the likelihood comes form the class of weighted distribution. Two kinds of perturbations are considered for the local sensitivity analysis. In addition, some numerical examples are considered which provide measures of robustness.

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Robust Bayes and Empirical Bayes Analysis in Finite Population Sampling with Auxiliary Information

  • Kim, Dal-Ho
    • Journal of the Korean Statistical Society
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    • 제27권3호
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    • pp.331-348
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    • 1998
  • In this paper, we have proposed some robust Bayes estimators using ML-II priors as well as certain empirical Bayes estimators in estimating the finite population mean in the presence of auxiliary information. These estimators are compared with the classical ratio estimator and a subjective Bayes estimator utilizing the auxiliary information in terms of "posterior robustness" and "procedure robustness" Also, we have addressed the issue of choice of sampling design from a robust Bayesian viewpoint.

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Bayesian Statistical Modeling of System Energy Saving Effectiveness for MAC Protocols of Wireless Sensor Networks: The Case of Non-Informative Prior Knowledge

  • Kim, Myong-Hee;Park, Man-Gon
    • 한국멀티미디어학회논문지
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    • 제13권6호
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    • pp.890-900
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    • 2010
  • The Bayesian networks methods provide an efficient tool for performing information fusion and decision making under conditions of uncertainty. This paper proposes Bayes estimators for the system effectiveness in energy saving of the wireless sensor networks by use of the Bayesian method under the non-informative prior knowledge about means of active and sleep times based on time frames of sensor nodes in a wireless sensor network. And then, we conduct a case study on some Bayesian estimation models for the system energy saving effectiveness of a wireless sensor network, and evaluate and compare the performance of proposed Bayesian estimates of the system effectiveness in energy saving of the wireless sensor network. In the case study, we have recognized that the proposed Bayesian system energy saving effectiveness estimators are excellent to adapt in evaluation of energy efficiency using non-informative prior knowledge from previous experience with robustness according to given values of parameters.

A Methodology for Estimating the Uncertainty in Model Parameters Applying the Robust Bayesian Inferences

  • Kim, Joo Yeon;Lee, Seung Hyun;Park, Tai Jin
    • Journal of Radiation Protection and Research
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    • 제41권2호
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    • pp.149-154
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    • 2016
  • Background: Any real application of Bayesian inference must acknowledge that both prior distribution and likelihood function have only been specified as more or less convenient approximations to whatever the analyzer's true belief might be. If the inferences from the Bayesian analysis are to be trusted, it is important to determine that they are robust to such variations of prior and likelihood as might also be consistent with the analyzer's stated beliefs. Materials and Methods: The robust Bayesian inference was applied to atmospheric dispersion assessment using Gaussian plume model. The scopes of contaminations were specified as the uncertainties of distribution type and parametric variability. The probabilistic distribution of model parameters was assumed to be contaminated as the symmetric unimodal and unimodal distributions. The distribution of the sector-averaged relative concentrations was then calculated by applying the contaminated priors to the model parameters. Results and Discussion: The sector-averaged concentrations for stability class were compared by applying the symmetric unimodal and unimodal priors, respectively, as the contaminated one based on the class of ${\varepsilon}$-contamination. Though ${\varepsilon}$ was assumed as 10%, the medians reflecting the symmetric unimodal priors were nearly approximated within 10% compared with ones reflecting the plausible ones. However, the medians reflecting the unimodal priors were approximated within 20% for a few downwind distances compared with ones reflecting the plausible ones. Conclusion: The robustness has been answered by estimating how the results of the Bayesian inferences are robust to reasonable variations of the plausible priors. From these robust inferences, it is reasonable to apply the symmetric unimodal priors for analyzing the robustness of the Bayesian inferences.

Mixture Bayesian Robust Design

  • Seo, Han-Son
    • 품질경영학회지
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    • 제34권1호
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    • pp.48-53
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    • 2006
  • Applying Bayesian optimal design principles is not easy when a prior distribution is not certain. We present a optimal design criterion which possibly yield a reasonably good design and also robust with respect to misspecification of the prior distributions. The criterion is applied to the problem of estimating the turning point of a quadratic regression. Exact mathematical results are presented under certain conditions on prior distributions. Computational results are given for some cases not satisfying our conditions.

A study on the Bayesian nonparametric model for predicting group health claims

  • Muna Mauliza;Jimin Hong
    • Communications for Statistical Applications and Methods
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    • 제31권3호
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    • pp.323-336
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    • 2024
  • The accurate forecasting of insurance claims is a critical component for insurers' risk management decisions. Hierarchical Bayesian parametric (BP) models can be used for health insurance claims forecasting, but they are unsatisfactory to describe the claims distribution. Therefore, Bayesian nonparametric (BNP) models can be a more suitable alternative to deal with the complex characteristics of the health insurance claims distribution, including heavy tails, skewness, and multimodality. In this study, we apply both a BP model and a BNP model to predict group health claims using simulated and real-world data for a private life insurer in Indonesia. The findings show that the BNP model outperforms the BP model in terms of claims prediction accuracy. Furthermore, our analysis highlights the flexibility and robustness of BNP models in handling diverse data structures in health insurance claims.

고품질의 3D 콘텐츠 제작을 위한 베이지안 접근방식의 사진측량기반 편위수정기법 개발 (Development of Photogrammetric Rectification Method Applying Bayesian Approach for High Quality 3D Contents Production)

  • 김재인;김태정
    • 방송공학회논문지
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    • 제18권1호
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    • pp.31-42
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    • 2013
  • 본 논문에서는 고품질의 3D 콘텐츠 제작에 있어 입체피로를 최소화하기 위한 영상의 수직시차 교정방법으로, 베이지안 접근방식을 적용한 사진측량기반의 강인 편위수정 기법을 제안하고자 한다. 영상의 수직시차 제거 과정은 크게 기하추정 단계와 에피폴라 변환 단계로 구성된다. 본 논문에서는 기하추정을 위해 사진측량에서 널리 활용되고 있는 공면조건 기반의 상대표정 알고리즘을 적용한다. 이때 상대표정 알고리즘에는 자동 정합점 추출에 따른 오정합과 위치오차에 강인성을 확보하기 위해 제약조건을 도입한 베이지안 접근방식을 적용하고자 하며, 이를 바탕으로 수행되는 에피폴라 변환에는 영상의 왜곡과 원 영상 대비 변형을 최소화하기 위한 공선조건기반의 중심투영변환기법을 적용하고자 한다. 알고리즘의 성능검증을 위한 비교 알고리즘으로, 기하추정에는 일반적인 상대표정 알고리즘과 컴퓨터비전분야의 8점 알고리즘 및 스테레오 캘리브레이션 기법이 사용되었으며, 에피폴라 변환에는 Hartley 방법과 Bouguet 방법이 사용되었다. 실험결과는 제안 알고리즘의 높은 정확도와 여러 오차요인들에 대한 강인성, 그리고 최소화된 영상변형의 결과를 보여주었다.