• Title/Summary/Keyword: Bayesian information

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Estimating a Binomial Proportion with Bayes Estimated Imputed Conditional Means

  • Shin, Min-Woong;Lee, Sang-Eun
    • Communications for Statistical Applications and Methods
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    • v.9 no.1
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    • pp.63-73
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    • 2002
  • The one of analytic imputation technique involving conditional means was mentioned by Schafer and Schenker(2000). And their derivations are based on asymptotic expansions of point estimator and their associated variance estimator, and the result of imputation can be thought of as first-order approximations to the estimators. Specially in this paper, we are presenting the method of estimating a Binomial proportion with Bayesian approach of imputed conditional means. That is, instead of using maximum likelihood(ML) estimator to estimate a Binomial proportion, in general, we use the Bayesian estimators and will show the result of estimated Imputed conditional means.

Diagnostics of Rotating Machinery using Recursive Bayesian Estimation (재귀 베이시안 추정을 이용한 회전기기 진단)

  • Oh, Joon-Seok;Sohn, Seok-Man;Kim, Hee-Soo;Lee, Seung-Cheol;Bae, Yong-Chae
    • KEPCO Journal on Electric Power and Energy
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    • v.6 no.1
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    • pp.49-52
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    • 2020
  • Since power plant is an important system to provide electricity, it is necessary to monitor it in order to operate safely. Much information related with machine diagnosis exists in written form instead of digital data. So, it causes difficulties of analyzing and finding solutions. Rulebased expert system can provide flexible and effective solutions to users. In this paper, Recursive Bayesian Estimation is applied in order to increase accuracy of solutions.

Filtering Technique of P2P Mobile Agent using Naive Bayesian Algorithm (Naive Bayesian 알고리즘을 이용한 P2P 모바일 에이전트의 필터링 기법)

  • Lee Se-Il;Lee Sang-Yong
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • 2005.04a
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    • pp.363-366
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    • 2005
  • 유비쿼터스 컴퓨팅에서 사용자에게 필요한 서비스를 지능적으로 제공하기 위해서는 컨텍스트 정보의 효과적인 필터링이 필요하다. 현재까지 사용되고 있는 필터링 기술은 온라인상에서 사용되는 사용자 정보를 기준으로 서비스를 제공하고 있다. 하지만 휴대용 유$\cdot$무선기기에서 컨텍스트 인식에 기반을 둔 서비스를 제공하기 위해서는 복잡한 필터링과정과 큰 저장 공간이 요구된다. 따라서 본 논문에서는 사용자 주변에 널려 있는 센서를 통해 입력된 컨텍스트 정보들을 효율적으로 필터링하여 사용자에게 필요한 서비스만을 제공하도록 하였다. 이를 위해서 기존의 P2P 모바일 에이전트에서 사용되는 협력적 필터링 기술에 Naive Bayesian 알고리즘을 혼합한 컨텍스트 협력적 필터링 알고리즘을 제안한다.

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New stereo matching algorithm based on probabilistic diffusion (확률적 확산을 이용한 스테레오 정합 알고리듬)

  • 이상화;이충웅
    • Journal of the Korean Institute of Telematics and Electronics S
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    • v.35S no.4
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    • pp.105-117
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    • 1998
  • In this paper, the general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The probabilistic models are independence and similarity among the neighboring disparities in the configuration.The formula is the generalized probabilistic diffusion equation based on Bayesian model, and can be implemented into the some different forms corresponding to the probabilistic models in the disparity neighborhood system or configuration. And, we proposed new probabilistic models in order to simplify the joint probability distribution of disparities in the configuration. According to the experimental results, the proposed algorithm outperformed the other ones, such as sum of swuared difference(SSD) based algorithm and Scharstein's method. We canconclude that the derived formular generalizes the probabilistic diffusion based on Bayesian MAP algorithm for disparity estimation, and the propsoed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to 0.01% of the generalized formula.

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Forecasting Demand of 5G Internet of things based on Bayesian Regression Model (베이지안 회귀모델을 활용한 5G 사물인터넷 수요 예측)

  • Park, Kyung Jin;Kim, Taehan
    • Journal of Information Technology Applications and Management
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    • v.26 no.2
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    • pp.61-73
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    • 2019
  • In 2019, 5G mobile communication technology will be commercialized. From the viewpoint of technological innovation, 5G service can be applied to other industries or developed further. Therefore, it is important to measure the demand of the Internet of things (IoT) because it is predicted to be commercialized widely in the 5G era and its demand hugely effects on the economic value of 5G industry. In this paper, we applied Bayesian method on regression model to find out the demand of 5G IoT service, wearable service in particular. As a result, we confirmed that the Bayesian regression model is closer to the actual value than the existing regression model. These findings can be utilized for predicting future demand of new industries.

Dynamic reliability analysis framework using fault tree and dynamic Bayesian network: A case study of NPP

  • Mamdikar, Mohan Rao;Kumar, Vinay;Singh, Pooja
    • Nuclear Engineering and Technology
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    • v.54 no.4
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    • pp.1213-1220
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    • 2022
  • The Emergency Diesel Generator (EDG) is a critical and essential part of the Nuclear Power Plant (NPP). Due to past catastrophic disasters, critical systems of NPP like EDG are designed to meet high dependability requirements. Therefore, we propose a framework for the dynamic reliability assessment using the Fault Tree and the Dynamic Bayesian Network. In this framework, the information of the component's failure probability is updated based on observed data. The framework is powerful to perform qualitative as well as quantitative analysis of the system. The validity of the framework is done by applying it on several NPP systems.

Forecasting the Baltic Dry Index Using Bayesian Variable Selection (베이지안 변수선택 기법을 이용한 발틱건화물운임지수(BDI) 예측)

  • Xiang-Yu Han;Young Min Kim
    • Korea Trade Review
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    • v.47 no.5
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    • pp.21-37
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    • 2022
  • Baltic Dry Index (BDI) is difficult to forecast because of the high volatility and complexity. To improve the BDI forecasting ability, this study apply Bayesian variable selection method with a large number of predictors. Our estimation results based on the BDI and all predictors from January 2000 to September 2021 indicate that the out-of-sample prediction ability of the ADL model with the variable selection is superior to that of the AR model in terms of point and density forecasting. We also find that critical predictors for the BDI change over forecasts horizon. The lagged BDI are being selected as an key predictor at all forecasts horizon, but commodity price, the clarksea index, and interest rates have additional information to predict BDI at mid-term horizon. This implies that time variations of predictors should be considered to predict the BDI.

Towards inferring reactor operations from high-level waste

  • Benjamin Jung;Antonio Figueroa;Malte Gottsche
    • Nuclear Engineering and Technology
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    • v.56 no.7
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    • pp.2704-2710
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    • 2024
  • Nuclear archaeology research provides scientific methods to reconstruct the operating histories of fissile material production facilities to account for past fissile material production. While it has typically focused on analyzing material in permanent reactor structures, spent fuel or high-level waste also hold information about the reactor operation. In this computational study, we explore a Bayesian inference framework for reconstructing the operational history from measurements of isotope ratios from a sample of nuclear waste. We investigate two different inference models. The first model discriminates between three potential reactors of origin (Magnox, PWR, and PHWR) while simultaneously reconstructing the fuel burnup, time since irradiation, initial enrichment, and average power density. The second model reconstructs the fuel burnup and time since irradiation of two batches of waste in a mixed sample. Each of the models is applied to a set of simulated test data, and the performance is evaluated by comparing the highest posterior density regions to the corresponding parameter values of the test dataset. Both models perform well on the simulated test cases, which highlights the potential of the Bayesian inference framework and opens up avenues for further investigation.

Bayesian Filter-Based Mobile Tracking under Realistic Network Setting (실제 네트워크를 고려한 베이지안 필터 기반 이동단말 위치 추적)

  • Kim, Hyowon;Kim, Sunwoo
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.41 no.9
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    • pp.1060-1068
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    • 2016
  • The range-free localization using connectivity information has problems of mobile tracking. This paper proposes two Bayesian filter-based mobile tracking algorithms considering a propagation scenario. Kalman and Markov Chain Monte Carlo (MCMC) particle filters are applied according to linearity of two measurement models. Measurement models of the Kalman and MCMC particle filter-based algorithms respectively are defined as connectivity between mobiles, information fusion of connectivity information and received signal strength (RSS) from neighbors within one-hop. To perform the accurate simulation, we consider a real indoor map of shopping mall and degree of radio irregularity (DOI) model. According to obstacles between mobiles, we assume two types of DOIs. We show the superiority of the proposed algorithm over existing range-free algorithms through MATLAB simulations.