• Title, Summary, Keyword: Bayesian

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On-line Bayesian Learning based on Wireless Sensor Network (무선 센서 네트워크에 기반한 온라인 베이지안 학습)

  • Lee, Ho-Suk
    • Proceedings of the Korean Information Science Society Conference
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    • pp.105-108
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    • 2007
  • Bayesian learning network is employed for diverse applications. This paper discusses the Bayesian learning network algorithm structure which can be applied in the wireless sensor network environment for various online applications. First, this paper discusses Bayesian parameter learning, Bayesian DAG structure learning, characteristics of wireless sensor network, and data gathering in the wireless sensor network. Second, this paper discusses the important considerations about the online Bayesian learning network and the conceptual structure of the learning network algorithm.

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Inferential Problems in Bayesian Logistic Regression Models (베이지안 로지스틱 회귀모형에서의 추론에 대한 연구)

  • Hwang, Jin-Soo;Kang, Sung-Chan
    • The Korean Journal of Applied Statistics
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    • v.24 no.6
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    • pp.1149-1160
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    • 2011
  • Model selection and hypothesis testing problems in Bayesian inference are still debated between scholars. Bayesian factors traditionally used as a criterion in Bayesian hypothesis testing and model selection, are easy to understand but sometimes hard to compute. In addition, there are other model selection criterions such as DIC(Deviance Information Criterion) by Spiegelhalter et al. (2002) and Bayesian P-values for testing. In this paper, we briefly introduce the Bayesian hypothesis testing and model selection procedure. In addition we have applied a Bayesian inference to Swiss banknote data by a fitting logistic regression model and computing several test statistics to see if they provide consistent results.

Bayesian methods in clinical trials with applications to medical devices

  • Campbell, Gregory
    • Communications for Statistical Applications and Methods
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    • v.24 no.6
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    • pp.561-581
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    • 2017
  • Bayesian statistics can play a key role in the design and analysis of clinical trials and this has been demonstrated for medical device trials. By 1995 Bayesian statistics had been well developed and the revolution in computing powers and Markov chain Monte Carlo development made calculation of posterior distributions within computational reach. The Food and Drug Administration (FDA) initiative of Bayesian statistics in medical device clinical trials, which began almost 20 years ago, is reviewed in detail along with some of the key decisions that were made along the way. Both Bayesian hierarchical modeling using data from previous studies and Bayesian adaptive designs, usually with a non-informative prior, are discussed. The leveraging of prior study data has been accomplished through Bayesian hierarchical modeling. An enormous advantage of Bayesian adaptive designs is achieved when it is accompanied by modeling of the primary endpoint to produce the predictive posterior distribution. Simulations are crucial to providing the operating characteristics of the Bayesian design, especially for a complex adaptive design. The 2010 FDA Bayesian guidance for medical device trials addressed both approaches as well as exchangeability, Type I error, and sample size. Treatment response adaptive randomization using the famous extracorporeal membrane oxygenation example is discussed. An interesting real example of a Bayesian analysis using a failed trial with an interesting subgroup as prior information is presented. The implications of the likelihood principle are considered. A recent exciting area using Bayesian hierarchical modeling has been the pediatric extrapolation using adult data in clinical trials. Historical control information from previous trials is an underused area that lends itself easily to Bayesian methods. The future including recent trends, decision theoretic trials, Bayesian benefit-risk, virtual patients, and the appalling lack of penetration of Bayesian clinical trials in the medical literature are discussed.

A Study on the Improvement of Bayesian networks in e-Trade (전자무역의 베이지안 네트워크 개선방안에 관한 연구)

  • Jeong, Boon-Do
    • International Commerce and Information Review
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    • v.9 no.3
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    • pp.305-320
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    • 2007
  • With expanded use of B2B(between enterprises), B2G(between enterprises and government) and EDI(Electronic Data Interchange), and increased amount of available network information and information protection threat, as it was judged that security can not be perfectly assured only with security technology such as electronic signature/authorization and access control, Bayesian networks have been developed for protection of information. Therefore, this study speculates Bayesian networks system, centering on ERP(Enterprise Resource Planning). The Bayesian networks system is one of the methods to resolve uncertainty in electronic data interchange and is applied to overcome uncertainty of abnormal invasion detection in ERP. Bayesian networks are applied to construct profiling for system call and network data, and simulate against abnormal invasion detection. The host-based abnormal invasion detection system in electronic trade analyses system call, applies Bayesian probability values, and constructs normal behavior profile to detect abnormal behaviors. This study assumes before and after of delivery behavior of the electronic document through Bayesian probability value and expresses before and after of the delivery behavior or events based on Bayesian networks. Therefore, profiling process using Bayesian networks can be applied for abnormal invasion detection based on host and network. In respect to transmission and reception of electronic documents, we need further studies on standards that classify abnormal invasion of various patterns in ERP and evaluate them by Bayesian probability values, and on classification of B2B invasion pattern genealogy to effectively detect deformed abnormal invasion patterns.

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Identification of Uncertainty in Fitting Rating Curve with Bayesian Regression (베이지안 회귀분석을 이용한 수위-유량 관계곡선의 불확실성 분석)

  • Kim, Sang-Ug;Lee, Kil-Seong
    • Journal of Korea Water Resources Association
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    • v.41 no.9
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    • pp.943-958
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    • 2008
  • This study employs Bayesian regression analysis for fitting discharge rating curves. The parameter estimates using the Bayesian regression analysis were compared to ordinary least square method using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian regression are not significantly different. However, the difference between upper and lower limits are remarkably reduced with the Bayesian regression. Therefore, from the point of view of uncertainty analysis, the Bayesian regression is more attractive than the conventional method based on a t-distribution because the data size at the site of interest is typically insufficient to estimate the parameters in rating curve. The merits and demerits of the two types of estimation methods are analyzed through the statistical simulation considering heteroscedasticity. The validation of the Bayesian regression is also performed using real stage-discharge data which were observed at 5 gauges on the Anyangcheon basin. Because the true parameters at 5 gauges are unknown, the quantitative accuracy of the Bayesian regression can not be assessed. However, it can be suggested that the uncertainty in rating curves at 5 gauges be reduced by Bayesian regression.

Structural health monitoring of Canton Tower using Bayesian framework

  • Kuok, Sin-Chi;Yuen, Ka-Veng
    • Smart Structures and Systems
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    • v.10 no.4_5
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    • pp.375-391
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    • 2012
  • This paper reports the structural health monitoring benchmark study results for the Canton Tower using Bayesian methods. In this study, output-only modal identification and finite element model updating are considered using a given set of structural acceleration measurements and the corresponding ambient conditions of 24 hours. In the first stage, the Bayesian spectral density approach is used for output-only modal identification with the acceleration time histories as the excitation to the tower is unknown. The modal parameters and the associated uncertainty can be estimated through Bayesian inference. Uncertainty quantification is important for determination of statistically significant change of the modal parameters and for weighting assignment in the subsequent stage of model updating. In the second stage, a Bayesian model updating approach is utilized to update the finite element model of the tower. The uncertain stiffness parameters can be obtained by minimizing an objective function that is a weighted sum of the square of the differences (residuals) between the identified modal parameters and the corresponding values of the model. The weightings distinguish the contribution of different residuals with different uncertain levels. They are obtained using the Bayesian spectral density approach in the first stage. Again, uncertainty of the stiffness parameters can be quantified with Bayesian inference. Finally, this Bayesian framework is applied to the 24-hour field measurements to investigate the variation of the modal and stiffness parameters under changing ambient conditions. Results show that the Bayesian framework successfully achieves the goal of the first task of this benchmark study.

Bayesian Method in Forecasting of time Series (Bayesian 시계열 예측방법에 관한 소고)

  • 박일근
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.7 no.10
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    • pp.47-51
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    • 1984
  • In many forecasting problem, there is little or no useful historical information available at the time the initial forecast is required, The propose of this paper is study on Bayesian Method in forecasting. I : Introduction. II : Bayesian estimation. III : Constant Model. IV : General time series Models. V : Conclusion. The Bayesian procedure are then used to revise parameter estimates when time series information is available, in this paper we give a general description of the bayesian approach and demonstrate the methodology with several specific cases.

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A Study on Bayesian p-values

  • Hwnag, Hyungtae;Oh, Heejung
    • Communications for Statistical Applications and Methods
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    • v.9 no.3
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    • pp.725-732
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    • 2002
  • P-values are often perceived as measurements of degree of compatibility between the current data and the hypothesized model. In this paper, a new concept of Bayesian p-values is proposed and studied under the non-informative prior distributions, which can be thought as the Bayesian counterparts of the classical p-values in the sense of using the concept of significance level. The performances of the proposed Bayesian p-values are compared with those of the classical p-values through several examples.

Regional Low Flow Frequency Analysis Using Bayesian Multiple Regression (Bayesian 다중회귀분석을 이용한 저수량(Low flow) 지역 빈도분석)

  • Kim, Sang-Ug;Lee, Kil-Seong
    • Journal of Korea Water Resources Association
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    • v.41 no.3
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    • pp.325-340
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    • 2008
  • This study employs Bayesian multiple regression analysis using the ordinary least squares method for regional low flow frequency analysis. The parameter estimates using the Bayesian multiple regression analysis were compared to conventional analysis using the t-distribution. In these comparisons, the mean values from the t-distribution and the Bayesian analysis at each return period are not significantly different. However, the difference between upper and lower limits is remarkably reduced using the Bayesian multiple regression. Therefore, from the point of view of uncertainty analysis, Bayesian multiple regression analysis is more attractive than the conventional method based on a t-distribution because the low flow sample size at the site of interest is typically insufficient to perform low flow frequency analysis. Also, we performed low flow prediction, including confidence interval, at two ungauged catchments in the Nakdong River basin using the developed Bayesian multiple regression model. The Bayesian prediction proves effective to infer the low flow characteristic at the ungauged catchment.

A Study on Noisy Speech Recognition Using a Bayesian Adaptation Method (Bayesian 적응 방식을 이용한 잡음음성 인식에 관한 연구)

  • 정용주
    • The Journal of the Acoustical Society of Korea
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    • v.20 no.2
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    • pp.21-26
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    • 2001
  • An expectation-maximization (EM) based Bayesian adaptation method for the mean of noise is proposed for noise-robust speech recognition. In the algorithm, the on-line testing utterances are used for the unsupervised Bayesian adaptation and the prior distribution of the noise mean is estimated using the off-line training data. For the noisy speech modeling, the parallel model combination (PMC) method is employed. The proposed method has shown to be effective compared with the conventional PMC method for the speech recognition experiments in a car-noise condition.

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